miχpods: MOM6 dev#
daily cycle diagnostics
more wind dependencies figures + timing of descent marks
check background mixing formulation
bootstrap error on mean, median?
fix EUC max at 110
figure out time base
shear spectra
make χ, ε mean profiles.
do ε-Ri?
plot mean, medians on histograms.
Notes:#
Diagnostic notes/improvements#
match vertical resolution and extent
restrict TAO S2 to ADCP depth range
restrict to top 200m.
Filtering out MLD makes a big difference!
SInce we’re working with derivatives does the vertical grid matter (as long as it is coarser than observations)?
1 difvho ocean_vertical_heat_diffusivity
2 difvso ocean_vertical_salt_diffusivity
Future model simulations#
Need lateral / neutral terms for total χ, ε
Add Atlantic TAO moorings + LOTUS, PAPA, others used in LMD94
Fix restart issue
Uncertainties#
Why can’t I reproduce the ε figure?
Why can’t I save the new station data
Need to consider Ri smoothing in models: From Gustavo:
You mentioned some kind of inconsistency between your diagnostics and what MOM6 was doing for the interior shear mixing scheme. I am working to implement the option to apply vertical smoothing in Ri multiple times, instead of only once which is what we are doing right now. I noticed that the diagnostic
ri_grad_shearis saved before the smoothing is applied. There is another diagnostic (ri_grad_shear_smooth) that saves the smoothed Ri. Perhaps you were looking at ri_grad_shear instead of ri_grad_shear_smooth and this can explain the inconsistency.
Diagnostics Effort notes#
go through
prepare; pass in dataset and optionalxgcm.Griddifferent pre-processing steps for different datasets
order, sign of z coordinate is painful; here normalizing
composing with matplotlib subfigures
References#
Warner & Moum (2019)
Setup#
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%load_ext autoreload
%load_ext rich
%load_ext watermark
import warnings
import cf_xarray as cfxr
import dask
import dask_jobqueue
import dcpy
import distributed
import flox.xarray
import holoviews as hv
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tqdm
import xgcm
import xrft
from datatree import DataTree
from mom6_tools.sections import preprocess_mom6_sections
import xarray as xr
%aimport pump
from pump import mixpods
hv.notebook_extension("bokeh")
cfxr.set_options(warn_on_missing_variables=False)
xr.set_options(keep_attrs=True, display_expand_data=False)
plt.style.use("bmh")
plt.rcParams["figure.dpi"] = 140
gcmdir = "/glade/campaign/cgd/oce/people/bachman/TPOS_1_20_20_year/OUTPUT/" # MITgcm output directory
stationdirname = gcmdir
mixing_layer_depth_criteria = {
"boundary_layer_depth": {"name": "KPPhbl|KPP_OBLdepth|ePBL_h_ML"},
}
from pump.catalog import catalog_dict
%watermark -iv
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The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
The rich extension is already loaded. To reload it, use:
%reload_ext rich
The watermark extension is already loaded. To reload it, use:
%reload_ext watermark
dcpy : 0.1.dev387+gd06c937
tqdm : 4.65.0
re : 2.2.1
pump : 1.0+247.g1f1c5e1.dirty
xrft : 0.0.0
xarray : 2023.3.0
dask_jobqueue: 0.7.3
holoviews : 1.15.4
intake : 0.6.8
dask : 2023.3.2
json : 2.0.9
datatree : 0.0.12
distributed : 2023.3.2
flox : 0.6.10
cf_xarray : 0.8.0
numpy : 1.23.5
matplotlib : 3.7.1
ipywidgets : 8.0.6
xgcm : 0.6.1
sys : 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0]
pandas : 1.5.3
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if "client" in locals():
client.close()
del client
if "cluster" in locals():
cluster.close()
cluster = dask_jobqueue.PBSCluster(
cores=4, # The number of cores you want
memory="23GB", # Amount of memory
processes=1, # How many processes
queue="casper", # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
local_directory="/local_scratch/pbs.$PBS_JOBID/dask/spill",
log_directory="/glade/scratch/dcherian/dask/",
resource_spec="select=1:ncpus=4:mem=23GB", # Specify resources
project="ncgd0011", # Input your project ID here
walltime="02:00:00", # Amount of wall time
interface="ib0", # Interface to use
)
cluster.adapt(minimum_jobs=1, maximum_jobs=4)
client = distributed.Client(cluster)
client
Show code cell output Hide code cell output
Client
Client-51ce0365-ef4e-11ed-b816-3cecef1f3772
| Connection method: Cluster object | Cluster type: dask_jobqueue.PBSCluster |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status |
Cluster Info
PBSCluster
ffb33b80
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status | Workers: 0 |
| Total threads: 0 | Total memory: 0 B |
Scheduler Info
Scheduler
Scheduler-dceb8a58-5aae-448b-afa0-f1e9ff26414b
| Comm: tcp://10.12.206.43:35516 | Workers: 0 |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status | Total threads: 0 |
| Started: Just now | Total memory: 0 B |
Workers
Read data#
LES#
%autoreload
les = mixpods.load_les_moorings()
--> The keys in the returned dictionary of datasets are constructed as follows:
'latitude.longitude.month.kind.length'
microstructure#
micro = mixpods.load_microstructure()
micro
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- depth: 200
- time: 2624
- zeuc: 80
- enso_transition_phase: 1
- N2T_bins: 29
- S2_bins: 29
- stat: 3
- Rig_T_bins: 9
- depth(depth)float64-200.0 -199.0 -198.0 ... -2.0 -1.0
- positive :
- up
- axis :
- Z
array([-200., -199., -198., -197., -196., -195., -194., -193., -192., -191., -190., -189., -188., -187., -186., -185., -184., -183., -182., -181., -180., -179., -178., -177., -176., -175., -174., -173., -172., -171., -170., -169., -168., -167., -166., -165., -164., -163., -162., -161., -160., -159., -158., -157., -156., -155., -154., -153., -152., -151., -150., -149., -148., -147., -146., -145., -144., -143., -142., -141., -140., -139., -138., -137., -136., -135., -134., -133., -132., -131., -130., -129., -128., -127., -126., -125., -124., -123., -122., -121., -120., -119., -118., -117., -116., -115., -114., -113., -112., -111., -110., -109., -108., -107., -106., -105., -104., -103., -102., -101., -100., -99., -98., -97., -96., -95., -94., -93., -92., -91., -90., -89., -88., -87., -86., -85., -84., -83., -82., -81., -80., -79., -78., -77., -76., -75., -74., -73., -72., -71., -70., -69., -68., -67., -66., -65., -64., -63., -62., -61., -60., -59., -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48., -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1.]) - lon(time)float64-139.9 -139.9 ... -139.9 -139.9
- standard_name :
- longitude
- units :
- degrees_east
array([-139.868406 , -139.86840867, -139.86840817, ..., -139.87721183, -139.87707 , -139.877121 ]) - lat(time)float640.06246 0.0622 ... 0.06317 0.06341
- standard_name :
- latitude
- units :
- degrees_north
array([0.06245817, 0.06219917, 0.06263083, ..., 0.06311433, 0.063169 , 0.0634125 ]) - time(time)datetime64[ns]2008-10-24T20:36:23 ... 2008-11-...
array(['2008-10-24T20:36:23.000000000', '2008-10-24T20:44:18.000000000', '2008-10-24T20:54:17.000000000', ..., '2008-11-08T18:58:49.000000000', '2008-11-08T19:06:14.000000000', '2008-11-08T19:13:47.000000000'], dtype='datetime64[ns]') - eucmax(time)float64nan nan nan nan ... nan nan nan nan
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
array([nan, nan, nan, ..., nan, nan, nan])
- mldT(time)float64-13.0 -11.0 -14.0 ... -33.0 -35.0
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
array([-13., -11., -14., ..., -33., -33., -35.])
- dcl_mask(depth, time)boolFalse False False ... False False
- description :
- True when 5m below mldT and above eucmax.
array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]]) - enso_transition_phase(enso_transition_phase)<U4'none'
array(['none'], dtype='<U4')
- N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - S2_bins(S2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} S^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - bin_areas(N2T_bins, S2_bins)float640.01 0.01 0.01 ... 0.01 0.01 0.01
- long_name :
- log$_{10} 4N_T^2$
array([[0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, ... 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]]) - stat(stat)object'mean' 'std' 'count'
array(['mean', 'std', 'count'], dtype=object)
- Rig_T_bins(Rig_T_bins)object(-1.6, -1.4000000000000001] ... ...
- long_name :
- $Ri^g_T$
array([Interval(-1.6, -1.4000000000000001, closed='right'), Interval(-1.4000000000000001, -1.2000000000000002, closed='right'), Interval(-1.2000000000000002, -1.0000000000000002, closed='right'), Interval(-1.0000000000000002, -0.8000000000000003, closed='right'), Interval(-0.8000000000000003, -0.6000000000000003, closed='right'), Interval(-0.6000000000000003, -0.40000000000000036, closed='right'), Interval(-0.40000000000000036, -0.2000000000000004, closed='right'), Interval(-0.2000000000000004, -4.440892098500626e-16, closed='right'), Interval(-4.440892098500626e-16, 0.1999999999999995, closed='right')], dtype=object)
- pmax(time)float64...
[2624 values with dtype=float64]
- castnumber(time)uint16...
[2624 values with dtype=uint16]
- AX_TILT(depth, time)float64...
[524800 values with dtype=float64]
- AY_TILT(depth, time)float64...
[524800 values with dtype=float64]
- AZ2(depth, time)float64...
[524800 values with dtype=float64]
- C(depth, time)float64...
[524800 values with dtype=float64]
- chi(depth, time)float64...
- long_name :
- $χ$
- units :
- °C²/s
[524800 values with dtype=float64]
- DRHODZ(depth, time)float64...
[524800 values with dtype=float64]
- dTdz(depth, time)float64...
[524800 values with dtype=float64]
- eps(depth, time)float64...
- long_name :
- $ε$
- units :
- W/kg
[524800 values with dtype=float64]
- EPSILON1(depth, time)float64...
[524800 values with dtype=float64]
- EPSILON2(depth, time)float64...
[524800 values with dtype=float64]
- FALLSPD(depth, time)float64...
[524800 values with dtype=float64]
- MHT(depth, time)float64...
[524800 values with dtype=float64]
- N2(depth, time)float64...
[524800 values with dtype=float64]
- pres(depth)float64...
- standard_name :
- sea_water_pressure
- units :
- dbar
- positive :
- down
[200 values with dtype=float64]
- salt(depth, time)float64...
- standard_name :
- sea_water_salinity
- units :
- psu
[524800 values with dtype=float64]
- SCAT(depth, time)float64...
[524800 values with dtype=float64]
- pden(depth, time)float64...
- standard_name :
- sea_water_potential_density
[524800 values with dtype=float64]
- SIGMA_ORDER(depth, time)float64...
[524800 values with dtype=float64]
- T(depth, time)float64...
- standard_name :
- sea_water_temperature
- units :
- celsius
[524800 values with dtype=float64]
- theta(depth, time)float64...
- standard_name :
- sea_water_potential_temperature
- units :
- celsius
[524800 values with dtype=float64]
- TP(depth, time)float64...
[524800 values with dtype=float64]
- VARAZ(depth, time)float64...
[524800 values with dtype=float64]
- VARLT(depth, time)float64...
[524800 values with dtype=float64]
- u(depth, time)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_x_velocity
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - v(depth, time)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_y_velocity
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - dudz(depth, time)float64...
[524800 values with dtype=float64]
- dvdz(depth, time)float64...
[524800 values with dtype=float64]
- Sh2(depth, time)float64...
[524800 values with dtype=float64]
- Jq(depth, time)float64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[524800 values with dtype=float64]
- dJdz(depth, time)float64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[524800 values with dtype=float64]
- dTdt(depth, time)float64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[524800 values with dtype=float64]
- mld(time)float64...
- long_name :
- MLD
- units :
- m
- description :
- Interpolate density to 1m grid. Search for min depth where |drho| > 0.005 and N2 > 1e-08
[2624 values with dtype=float64]
- Jq_euc(time, zeuc)float64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[209920 values with dtype=float64]
- dJdz_euc(time, zeuc)float64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[209920 values with dtype=float64]
- dTdt_euc(time, zeuc)float64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[209920 values with dtype=float64]
- u_euc(time, zeuc)float64...
[209920 values with dtype=float64]
- depth_euc(time, zeuc)float64...
- positive :
- down
- axis :
- Z
[209920 values with dtype=float64]
- count_Jq_euc(time, zeuc)int64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[209920 values with dtype=int64]
- count_dJdz_euc(time, zeuc)int64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[209920 values with dtype=int64]
- count_dTdt_euc(time, zeuc)int64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[209920 values with dtype=int64]
- count_u_euc(time, zeuc)int64...
[209920 values with dtype=int64]
- count_depth_euc(time, zeuc)int64...
- positive :
- down
- axis :
- Z
[209920 values with dtype=int64]
- S2(depth, time)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - densT(depth, time)float6426.3 nan 26.31 ... 23.13 23.13
- standard_name :
- sea_water_potential_density
array([[26.297089, nan, 26.312185, ..., 26.266018, 26.260968, 26.233753], [26.295396, 26.313673, 26.31144 , ..., 26.258406, 26.251399, 26.228656], [26.293885, 26.312763, 26.310515, ..., 26.250566, 26.248457, 26.222315], ..., [23.545845, 23.732602, 23.73726 , ..., 23.133029, 23.132049, 23.13409 ], [23.512819, 23.732427, 23.729974, ..., 23.13317 , 23.130685, 23.133259], [23.460799, 23.731189, nan, ..., 23.131709, 23.131449, 23.134385]]) - Tz(depth, time)float640.0004451 nan ... -0.002449
array([[ 4.45085408e-04, nan, 3.07681815e-03, ..., 7.22376397e-02, 8.30999061e-02, 4.76147796e-02], [-3.83851325e-04, nan, 1.40913361e-03, ..., 6.84409864e-02, 5.80820908e-02, 4.86450054e-02], [ 1.15764758e-03, -2.09826135e-03, -1.34532664e-03, ..., 6.61836306e-02, 4.37712871e-02, 4.35441644e-02], ..., [ 5.32282618e-05, 2.01791337e-03, 1.42449590e-02, ..., 6.43031298e-03, 2.48703533e-03, 1.12428809e-03], [ 3.29573573e-03, 8.68799643e-04, nan, ..., 9.44657253e-04, 1.67830944e-03, -6.44043644e-04], [ 1.26862559e-03, 1.18666970e-03, nan, ..., 1.25686323e-03, 8.56585581e-04, -2.44915480e-03]]) - Sz(depth, time)float64-0.00207 nan ... 0.001359 0.0004979
array([[-2.06964021e-03, nan, -1.44946119e-04, ..., 9.26641250e-03, 9.63816695e-03, 6.15793613e-03], [-2.17177735e-03, nan, -7.04573518e-04, ..., 8.13833828e-03, 7.30635586e-03, 5.64574174e-03], [-1.55824232e-03, -1.81640561e-03, -1.37623647e-03, ..., 8.46887347e-03, 5.30300921e-03, 2.99761519e-03], ..., [-3.19977016e-02, 3.82442847e-05, 3.09477295e-04, ..., 9.87609769e-04, -4.06957009e-04, 1.83408508e-04], [-5.49066091e-02, -5.89258743e-04, nan, ..., -4.90292835e-04, 2.83796097e-04, -6.55212175e-05], [-6.82660462e-02, -1.16535649e-03, nan, ..., -1.42506629e-03, 1.35935074e-03, 4.97874481e-04]]) - N2T(depth, time)float641.621e-05 nan ... -1.078e-05
array([[ 1.62069255e-05, nan, 7.13030500e-06, ..., 7.28554431e-05, 9.15777799e-05, 4.87847311e-05], [ 1.53328912e-05, nan, 7.99307540e-06, ..., 7.39429862e-05, 5.98694601e-05, 5.47360405e-05], [ 1.38204706e-05, 9.32659785e-06, 7.54644347e-06, ..., 6.75034433e-05, 4.69156145e-05, 6.43892107e-05], ..., [ 2.31820485e-04, 5.52064722e-06, 3.86941621e-05, ..., 1.17302055e-05, 1.02388126e-05, 1.97236791e-06], [ 4.06978935e-04, 6.76330647e-06, nan, ..., 6.31643452e-06, 2.87322630e-06, -1.41564212e-06], [ 4.97875830e-04, 1.18483355e-05, nan, ..., 1.39894751e-05, -7.31214667e-06, -1.07834292e-05]]) - shred2(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - Rig_T(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Ri^g_T$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - Rig(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Ri^g$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - n2s2pdf(enso_transition_phase, N2T_bins, S2_bins)float640.0005791 0.002316 ... 0.0 0.0
- long_name :
- $P(S^2, 4N_T^2)$
array([[[5.79082386e-04, 2.31632954e-03, 2.31632954e-03, 2.89541193e-03, 2.89541193e-03, 4.63265909e-03, 5.21174147e-03, 6.94898863e-03, 6.36990625e-03, 5.79082386e-03, 1.21607301e-02, 8.10715340e-03, 1.15816477e-02, 1.67933892e-02, 1.50561420e-02, 9.84440056e-03, 1.44770597e-02, 5.79082386e-03, 2.31632954e-03, 4.05357670e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [5.21174147e-03, 5.21174147e-03, 3.47449432e-03, 3.47449432e-03, 5.21174147e-03, 6.94898863e-03, 8.68623579e-03, 5.79082386e-03, 1.10025653e-02, 1.21607301e-02, 1.04234829e-02, 1.79515540e-02, 1.73724716e-02, 1.56352244e-02, 1.56352244e-02, 1.38979773e-02, 9.84440056e-03, 9.84440056e-03, 4.63265909e-03, 4.05357670e-03, 1.15816477e-03, 2.89541193e-03, 5.79082386e-04, 5.79082386e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [8.68623579e-03, 6.94898863e-03, 4.05357670e-03, 9.84440056e-03, 8.68623579e-03, 9.84440056e-03, 6.36990625e-03, 1.38979773e-02, 1.85306364e-02, 1.73724716e-02, 1.85306364e-02, 2.08469659e-02, 2.14260483e-02, 2.54796250e-02, 2.25842131e-02, 1.67933892e-02, ... 7.52807102e-03, 5.79082386e-03, 9.84440056e-03, 2.20051307e-02, 3.47449432e-02, 4.34311790e-02, 4.92220028e-02, 3.01122841e-02, 1.04234829e-02, 2.31632954e-03, 1.15816477e-03, 5.79082386e-04, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 1.15816477e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.79082386e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.79082386e-04, 0.00000000e+00, 1.15816477e-03, 2.31632954e-03, 1.15816477e-03, 4.05357670e-03, 7.52807102e-03, 1.62143068e-02, 2.89541193e-02, 3.64821903e-02, 2.77959545e-02, 1.04234829e-02, 1.15816477e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.15816477e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.73724716e-03, 1.73724716e-03, 2.31632954e-03, 6.36990625e-03, 1.50561420e-02, 1.21607301e-02, 5.21174147e-03, 1.73724716e-03, 5.79082386e-04, 5.79082386e-04, 0.00000000e+00, 0.00000000e+00]]]) - eps_ri(enso_transition_phase, stat, Rig_T_bins)float642.696e-06 2.284e-06 ... 2.266e+03
- long_name :
- $ε$
- units :
- W/kg
array([[[2.69589647e-06, 2.28374757e-06, 1.88012435e-06, 1.45285505e-06, 1.13398965e-06, 1.15853540e-06, 1.48043118e-06, 1.89143965e-06, 1.94550419e-06], [5.82257052e-06, 5.34773637e-06, 4.78679450e-06, 4.02680454e-06, 3.67176061e-06, 3.64904837e-06, 4.87936972e-06, 5.86937368e-06, 6.22798022e-06], [1.53200000e+03, 2.96800000e+03, 6.07700000e+03, 1.33270000e+04, 2.46880000e+04, 2.57650000e+04, 1.37950000e+04, 5.44500000e+03, 2.26600000e+03]]])
- starttime :
- ['Time:20:34:29 298 ' 'Time:20:42:18 298 ' 'Time:20:52:14 298 ' ... 'Time:18:56:50 313 ' 'Time:19:04:01 313 ' 'Time:19:11:46 313 ']
- endtime :
- ['Time:20:38:29 298 ' 'Time:20:46:29 298 ' 'Time:20:56:29 298 ' ... 'Time:19:01:00 313 ' 'Time:19:08:40 313 ' 'Time:19:16:00 313 ']
<xarray.DatasetView> Dimensions: (depth: 200, time: 2624, zeuc: 80, enso_transition_phase: 1, N2T_bins: 29, S2_bins: 29, stat: 3, Rig_T_bins: 9) Coordinates: (12/13) * depth (depth) float64 -200.0 -199.0 -198.0 ... -2.0 -1.0 lon (time) float64 -139.9 -139.9 -139.9 ... -139.9 -139.9 lat (time) float64 0.06246 0.0622 ... 0.06317 0.06341 * time (time) datetime64[ns] 2008-10-24T20:36:23 ... 2008... eucmax (time) float64 nan nan nan nan ... nan nan nan nan mldT (time) float64 -13.0 -11.0 -14.0 ... -33.0 -35.0 ... ... * enso_transition_phase (enso_transition_phase) <U4 'none' * N2T_bins (N2T_bins) object [-5.0, -4.9) ... [-2.20000000000... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200000000000... bin_areas (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01 * stat (stat) object 'mean' 'std' 'count' * Rig_T_bins (Rig_T_bins) object (-1.6, -1.4000000000000001] ..... Dimensions without coordinates: zeuc Data variables: (12/54) pmax (time) float64 ... castnumber (time) uint16 ... AX_TILT (depth, time) float64 ... AY_TILT (depth, time) float64 ... AZ2 (depth, time) float64 ... C (depth, time) float64 ... ... ... N2T (depth, time) float64 1.621e-05 nan ... -1.078e-05 shred2 (depth, time) float64 nan nan nan nan ... nan nan nan Rig_T (depth, time) float64 nan nan nan nan ... nan nan nan Rig (depth, time) float64 nan nan nan nan ... nan nan nan n2s2pdf (enso_transition_phase, N2T_bins, S2_bins) float64 ... eps_ri (enso_transition_phase, stat, Rig_T_bins) float64 ... Attributes: starttime: ['Time:20:34:29 298 ' 'Time:20:42:18 298 ' 'Time:20:52:14... endtime: ['Time:20:38:29 298 ' 'Time:20:46:29 298 ' 'Time:20:56:29...equix- time: 287
- depth: 60
- zeuc: 80
- enso_transition_phase: 1
- N2T_bins: 29
- S2_bins: 29
- stat: 3
- Rig_T_bins: 9
- time(time)datetime64[ns]1984-11-19T20:30:02 ... 1984-12-...
array(['1984-11-19T20:30:02.000000000', '1984-11-19T21:29:57.000000000', '1984-11-19T22:30:00.000000000', ..., '1984-12-01T16:25:49.000000000', '1984-12-01T17:25:52.000000000', '1984-12-01T18:25:46.000000000'], dtype='datetime64[ns]') - lat(time)float64-0.0355 -0.012 ... 0.0028 -0.0127
- standard_name :
- latitude
- units :
- degrees_north
array([-0.0355, -0.012 , -0.0043, -0.0228, 0.0048, -0.0037, -0.0164, -0.018 , -0.0158, 0.0017, -0.019 , 0.0048, -0.0023, -0.0045, -0.0007, -0.0103, 0.0045, -0.0019, -0.0091, 0.0058, 0.0058, -0.0032, -0.0137, -0.0137, -0.0103, -0.0108, -0.0156, 0.0118, 0.0127, 0.0128, -0.0018, -0.0147, 0.0045, -0.0065, 0.0048, -0.0086, 0.0042, 0.0051, 0.006 , 0.007 , 0.0079, 0.0058, 0.0012, -0.0037, 0.002 , -0.0058, 0.0068, -0.0053, -0.005 , 0.0007, -0.0078, -0.0088, 0.0043, 0.0228, -0.0035, -0.0152, -0.0023, -0.003 , 0.0017, 0.0023, 0.01 , 0.0088, 0.0076, 0.0064, 0.0052, 0.004 , 0.0028, -0.0009, -0.0162, 0.005 , -0.008 , 0.0063, -0.0018, 0.0055, 0.0001, -0.0027, 0.0163, -0.0071, 0.0053, 0.0037, 0.0042, -0.0025, -0.0052, -0.006 , -0.0168, 0.0029, -0.0068, -0.0064, -0.0019, 0.0158, 0.0161, 0.0079, 0.0048, -0.0054, -0.0127, 0.0077, 0.0144, 0.0107, 0.001 , 0.0184, 0.0246, 0.0167, 0.0135, 0.0018, 0.0064, -0.0014, 0.0031, 0.0068, 0.0013, 0.0026, 0.0039, 0.0051, 0.0056, 0.0055, 0.0039, 0.0016, -0.0057, -0.0096, -0.004 , -0.0008, 0.0021, 0.0029, 0.0038, 0.0047, 0.0056, 0.0065, 0.0085, 0.0059, 0.0085, 0.0126, 0.0099, 0.014 , -0.0003, -0.0067, -0.0047, -0.0047, -0.0047, 0.0083, 0.0057, 0.0078, ... -0.0043, -0.0062, 0.0056, 0.0056, 0.0052, 0.0084, 0.0138, 0.0043, 0.0073, 0.0106, 0.01 , 0.0093, 0.0087, 0.0079, 0.0071, 0.0046, 0.0002, -0.0036, 0.0114, 0.0136, 0.0117, 0.0097, 0.0078, 0.0059, 0.0039, 0.002 , 0.0001, -0.0019, -0.0052, 0.016 , 0.0114, 0.0102, 0.009 , 0.0078, 0.0067, 0.0055, 0.0044, 0.0032, 0.0029, -0.004 , -0.0076, 0.0156, 0.0125, 0.01 , 0.0085, 0.0127, 0.0117, 0.0113, 0.0123, 0.0127, 0.0118, 0.0102, 0.0068, 0.007 , 0.008 , 0.0053, 0.0045, 0.0103, 0.011 , 0.0132, 0.0143, 0.0132, 0.011 , 0.0058, 0.0045, 0.0042, 0.002 , 0.0143, 0.0072, 0.0119, 0.0127, 0.0103, 0.0055, 0.0058, 0.0037, 0.01 , 0.0172, 0.0115, 0.012 , 0.005 , 0.0027, -0.001 , 0.0147, 0.0088, 0.0023, -0.0044, 0.0073, 0.0017, -0.0033, 0.0113, 0.0044, -0.0083, 0.0085, 0.0038, 0.0144, 0.0178, 0.0046, 0.0019, 0.0082, 0.007 , 0.0068, -0.0053, -0.012 , 0.0033, -0.004 , -0.0121, 0.0077, 0.0043, -0.006 , 0.0058, -0.0108, 0.0002, -0.0159, -0.0012, -0.0155, -0.0032, 0.0053, 0.0013, -0.0079, -0.0018, 0.0137, -0.0108, 0.0012, -0.0015, -0.0077, -0.006 , -0.0272, -0.0095, -0.0068, -0.0125, 0.001 , -0.0097, 0.0035, -0.016 , -0.0027, -0.0142, 0.0008, -0.012 , 0.0028, -0.0127]) - lon(time)float64140.0 140.0 140.0 ... 140.0 140.0
- standard_name :
- longitude
- units :
- degrees_east
array([140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140., 140.]) - depth(depth)float64-239.1 -235.1 -231.1 ... -7.1 -3.1
- positive :
- up
- axis :
- Z
array([-239.1, -235.1, -231.1, -227.1, -223.1, -219.1, -215.1, -211.1, -207.1, -203.1, -199.1, -195.1, -191.1, -187.1, -183.1, -179.1, -175.1, -171.1, -167.1, -163.1, -159.1, -155.1, -151.1, -147.1, -143.1, -139.1, -135.1, -131.1, -127.1, -123.1, -119.1, -115.1, -111.1, -107.1, -103.1, -99.1, -95.1, -91.1, -87.1, -83.1, -79.1, -75.1, -71.1, -67.1, -63.1, -59.1, -55.1, -51.1, -47.1, -43.1, -39.1, -35.1, -31.1, -27.1, -23.1, -19.1, -15.1, -11.1, -7.1, -3.1]) - eucmax(time)float64-119.1 -119.1 ... -107.1 -107.1
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
array([-119.1, -119.1, -119.1, nan, -115.1, -119.1, -119.1, -119.1, -119.1, -115.1, -123.1, -123.1, -123.1, -123.1, -127.1, -127.1, -127.1, -123.1, -123.1, -127.1, -135.1, -139.1, -135.1, -131.1, -131.1, nan, nan, nan, nan, -139.1, -139.1, nan, -147.1, -143.1, -143.1, -147.1, -151.1, -151.1, -151.1, -147.1, -147.1, -147.1, nan, -147.1, -151.1, -151.1, -151.1, -147.1, nan, nan, nan, nan, nan, -143.1, -151.1, -155.1, -151.1, -151.1, -151.1, -151.1, -147.1, -147.1, -143.1, -143.1, -139.1, -139.1, -143.1, -139.1, -139.1, -139.1, -135.1, -135.1, -131.1, -127.1, -123.1, -123.1, -123.1, -123.1, -123.1, -123.1, -123.1, -123.1, -119.1, -119.1, -119.1, -123.1, -119.1, -115.1, -111.1, -111.1, -115.1, -115.1, -119.1, -119.1, -119.1, -119.1, -115.1, nan, nan, -107.1, -107.1, -103.1, -107.1, -111.1, -115.1, nan, -119.1, nan, -115.1, -111.1, -115.1, -123.1, -123.1, -115.1, -111.1, -111.1, -111.1, -111.1, -115.1, -115.1, nan, nan, nan, -123.1, -119.1, -123.1, nan, nan, nan, nan, -115.1, -115.1, -119.1, -131.1, -123.1, -119.1, -111.1, -111.1, -111.1, -107.1, -103.1, -103.1, -103.1, -103.1, nan, -107.1, -111.1, -115.1, -115.1, -111.1, -107.1, -107.1, -107.1, -107.1, -111.1, -119.1, -115.1, -119.1, -115.1, -111.1, -111.1, -111.1, -107.1, -107.1, -107.1, -107.1, -111.1, -115.1, -119.1, -119.1, -115.1, -115.1, -115.1, -115.1, -115.1, -111.1, -107.1, -107.1, -107.1, -115.1, -115.1, -119.1, -115.1, -115.1, -115.1, -111.1, -107.1, -107.1, -111.1, -111.1, -111.1, -115.1, -119.1, -123.1, -119.1, -115.1, -111.1, -107.1, -107.1, -103.1, -107.1, -107.1, -103.1, -103.1, -107.1, -111.1, -111.1, -111.1, -115.1, -111.1, -111.1, -111.1, -111.1, -111.1, -111.1, nan, -115.1, -119.1, -123.1, -123.1, -119.1, -119.1, -115.1, -119.1, -123.1, -119.1, -119.1, -119.1, -123.1, -127.1, -131.1, -131.1, -135.1, -131.1, -127.1, -127.1, -131.1, -131.1, nan, -123.1, -123.1, -123.1, -123.1, -123.1, -123.1, -119.1, -123.1, -123.1, -127.1, -123.1, -119.1, -115.1, -115.1, -115.1, -123.1, -123.1, -119.1, -119.1, -123.1, -119.1, -119.1, -123.1, nan, -119.1, -115.1, -115.1, -119.1, -119.1, -123.1, -123.1, -119.1, -119.1, -119.1, -119.1, -119.1, -115.1, -115.1, -115.1, -111.1, -111.1, -107.1, -111.1, -111.1, -111.1, -111.1, -107.1, -107.1]) - mldT(time)float64-10.0 -10.0 -9.0 ... -28.0 -30.0
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
array([-10., -10., -9., nan, -9., -8., -9., -10., -14., -11., -15., -16., -17., -21., -22., -25., -25., -24., -24., -25., -26., -26., -21., -21., -14., nan, nan, nan, nan, -7., -7., nan, -10., -11., -13., -18., -21., -22., -20., -22., -22., -22., nan, -24., -25., -21., -21., -21., nan, nan, nan, nan, nan, -14., -17., -21., -22., -22., -26., -29., -26., -25., -27., -28., -28., -26., -30., -31., -35., -32., -30., -26., -18., -12., -12., -12., -13., -13., -17., -19., -26., -26., -25., -28., -29., -28., -29., -29., nan, nan, -28., -32., -31., -23., -30., -21., -29., nan, nan, -14., -15., -20., -24., -32., nan, nan, -23., nan, -24., -23., -25., -27., -30., -32., nan, -22., -25., -25., -23., -24., nan, nan, nan, nan, -14., -19., nan, nan, nan, nan, -25., -27., -23., -29., nan, -27., -24., -31., -26., -21., -20., -19., nan, -22., nan, -19., -16., -18., -19., -21., -21., -28., -27., -27., -26., -34., -32., nan, -31., -30., -33., nan, nan, -29., -29., -24., -28., -22., -19., -12., -10., -10., -9., -10., -12., -13., -17., -19., -22., -24., -17., -27., -26., -26., -28., -25., -24., -25., -23., -22., -22., -22., -16., -11., -11., -11., -10., -11., -13., -14., -17., -16., -19., -16., -22., -24., -25., -26., -24., -23., -27., -27., -28., -27., -23., nan, -17., -14., -14., -13., -15., -16., -19., -24., -24., -25., -25., -27., -25., -28., -32., -37., -36., -34., -34., -34., -39., nan, nan, -27., -22., -18., nan, nan, nan, -16., -17., -25., -24., -30., -28., -29., -33., -24., -28., -32., -32., -34., -34., -33., -32., -35., nan, -31., -25., -21., -17., -18., -19., -21., -24., -25., -28., -29., -30., -24., -28., -27., -31., -33., -32., -31., -33., -33., -33., -28., -30.]) - dcl_mask(depth, time)boolFalse False False ... False False
- description :
- True when 5m below mldT and above eucmax.
array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]]) - enso_transition_phase(enso_transition_phase)<U4'none'
array(['none'], dtype='<U4')
- N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - S2_bins(S2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} S^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - bin_areas(N2T_bins, S2_bins)float640.01 0.01 0.01 ... 0.01 0.01 0.01
- long_name :
- log$_{10} 4N_T^2$
array([[0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, ... 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]]) - stat(stat)object'mean' 'std' 'count'
array(['mean', 'std', 'count'], dtype=object)
- Rig_T_bins(Rig_T_bins)object(-1.6, -1.4000000000000001] ... ...
- long_name :
- $Ri^g_T$
array([Interval(-1.6, -1.4000000000000001, closed='right'), Interval(-1.4000000000000001, -1.2000000000000002, closed='right'), Interval(-1.2000000000000002, -1.0000000000000002, closed='right'), Interval(-1.0000000000000002, -0.8000000000000003, closed='right'), Interval(-0.8000000000000003, -0.6000000000000003, closed='right'), Interval(-0.6000000000000003, -0.40000000000000036, closed='right'), Interval(-0.40000000000000036, -0.2000000000000004, closed='right'), Interval(-0.2000000000000004, -4.440892098500626e-16, closed='right'), Interval(-4.440892098500626e-16, 0.1999999999999995, closed='right')], dtype=object)
- wspeed(time)float64...
[287 values with dtype=float64]
- T(depth, time)float64...
- standard_name :
- sea_water_temperature
- units :
- celsius
[17220 values with dtype=float64]
- salt(depth, time)float64...
- standard_name :
- sea_water_salinity
- units :
- psu
[17220 values with dtype=float64]
- pden(depth, time)float64...
- standard_name :
- sea_water_potential_density
[17220 values with dtype=float64]
- u(depth, time)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_x_velocity
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - v(depth, time)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_y_velocity
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - eps(depth, time)float64...
- long_name :
- $ε$
- units :
- W/kg
[17220 values with dtype=float64]
- dTdz(depth, time)float64...
[17220 values with dtype=float64]
- dsigdz(depth, time)float64...
[17220 values with dtype=float64]
- N2(depth, time)float64...
[17220 values with dtype=float64]
- dudz(depth, time)float64...
[17220 values with dtype=float64]
- dvdz(depth, time)float64...
[17220 values with dtype=float64]
- pres(depth)float64...
- standard_name :
- sea_water_pressure
- units :
- dbar
[60 values with dtype=float64]
- theta(depth, time)float64...
- standard_name :
- sea_water_potential_temperature
- units :
- celsius
[17220 values with dtype=float64]
- Jq(depth, time)float64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[17220 values with dtype=float64]
- dJdz(depth, time)float64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[17220 values with dtype=float64]
- dTdt(depth, time)float64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[17220 values with dtype=float64]
- mld(time)float64...
- long_name :
- MLD
- units :
- m
- description :
- Interpolate density to 1m grid. Search for min depth where |drho| > 0.005 and N2 > 1e-08
[287 values with dtype=float64]
- gamma_n(time, depth)float64...
- standard_name :
- neutral_density
- units :
- kg/m3
- long_name :
- $γ_n$
[17220 values with dtype=float64]
- Jq_euc(time, zeuc)float64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[22960 values with dtype=float64]
- dJdz_euc(time, zeuc)float64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[22960 values with dtype=float64]
- dTdt_euc(time, zeuc)float64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[22960 values with dtype=float64]
- u_euc(time, zeuc)float64...
[22960 values with dtype=float64]
- count_Jq_euc(time, zeuc)int64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[22960 values with dtype=int64]
- count_dJdz_euc(time, zeuc)int64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[22960 values with dtype=int64]
- count_dTdt_euc(time, zeuc)int64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[22960 values with dtype=int64]
- count_u_euc(time, zeuc)int64...
[22960 values with dtype=int64]
- S2(depth, time)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - densT(depth, time)float64nan nan nan ... 1.023e+03 1.023e+03
- standard_name :
- sea_water_potential_density
array([[ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], ..., [1023.511299, 1023.504299, 1023.496599, ..., 1023.466 , 1023.476101, 1023.4841 ], [1023.4751 , 1023.480301, 1023.4764 , ..., 1023.466299, 1023.475 , 1023.4841 ], [1023.4585 , 1023.452801, 1023.4321 , ..., 1023.4667 , 1023.4748 , 1023.481899]]) - Tz(depth, time)float64nan nan nan ... 0.0001676 0.002592
array([[ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], ..., [2.46847785e-02, 1.90100987e-02, 1.84975453e-02, ..., 4.55208378e-04, 1.43012708e-03, 1.36733731e-03], [2.11857197e-02, 2.16109714e-02, 2.69984736e-02, ..., 5.34247086e-06, 5.79942627e-04, 1.19216691e-03], [1.38364531e-02, 2.40113968e-02, 3.81611375e-02, ..., 4.26980477e-05, 1.67583434e-04, 2.59198996e-03]]) - Sz(depth, time)float64nan nan ... -0.0001002 0.0003003
array([[ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], ..., [-1.350500e-03, -1.074750e-03, -1.012375e-03, ..., -1.237500e-05, -2.475000e-05, 8.725000e-05], [-5.126250e-04, -1.250000e-04, -1.125000e-04, ..., 1.237500e-05, -6.250000e-05, 6.237500e-05], [-1.745000e-04, 2.497500e-04, 2.747500e-04, ..., 4.950000e-05, -1.002500e-04, 3.002500e-04]]) - N2T(depth, time)float64nan nan nan ... 4.785e-07 5.266e-06
array([[ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], ..., [ 7.91978049e-05, 6.10122183e-05, 5.92189024e-05, ..., 7.19001219e-07, 3.70865854e-06, 2.51231707e-06], [ 6.31656329e-05, 6.16091927e-05, 7.71628280e-05, ..., -8.37439024e-07, 1.55644024e-06, 2.63314756e-06], [ 3.97185366e-05, 6.57987805e-05, 1.05995854e-04, ..., -9.59465853e-07, 4.78536586e-07, 5.26629512e-06]]) - shred2(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - Rig_T(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Ri^g_T$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - Rig(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Ri^g$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - n2s2pdf(enso_transition_phase, N2T_bins, S2_bins)float640.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
- long_name :
- $P(S^2, 4N_T^2)$
array([[[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , ... 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]]]) - eps_ri(enso_transition_phase, stat, Rig_T_bins)float64nan nan 1.862e-07 ... 121.0 38.0
- long_name :
- $ε$
- units :
- W/kg
array([[[ nan, nan, 1.86240000e-07, 2.46591019e-07, 1.17969501e-07, 1.49295084e-07, 1.40315634e-07, 8.58288866e-08, 4.71714263e-08], [ nan, nan, 0.00000000e+00, 3.65708238e-07, 1.66410472e-07, 2.40937424e-07, 2.70345469e-07, 2.08883283e-07, 1.33746610e-07], [0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 1.60000000e+01, 2.09000000e+02, 1.17600000e+03, 7.47000000e+02, 1.21000000e+02, 3.80000000e+01]]])
- readme :
- ['this file is compiled from a binary ' 'Fortran file (converted to ascii beforehand):' 'th84ts.m4 --> th84ts.txt ']
- name :
- Tropic Heat
<xarray.DatasetView> Dimensions: (time: 287, depth: 60, zeuc: 80, enso_transition_phase: 1, N2T_bins: 29, S2_bins: 29, stat: 3, Rig_T_bins: 9) Coordinates: (12/13) * time (time) datetime64[ns] 1984-11-19T20:30:02 ... 1984... lat (time) float64 -0.0355 -0.012 ... 0.0028 -0.0127 lon (time) float64 140.0 140.0 140.0 ... 140.0 140.0 * depth (depth) float64 -239.1 -235.1 -231.1 ... -7.1 -3.1 eucmax (time) float64 -119.1 -119.1 -119.1 ... -107.1 -107.1 mldT (time) float64 -10.0 -10.0 -9.0 ... -33.0 -28.0 -30.0 ... ... * enso_transition_phase (enso_transition_phase) <U4 'none' * N2T_bins (N2T_bins) object [-5.0, -4.9) ... [-2.20000000000... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200000000000... bin_areas (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01 * stat (stat) object 'mean' 'std' 'count' * Rig_T_bins (Rig_T_bins) object (-1.6, -1.4000000000000001] ..... Dimensions without coordinates: zeuc Data variables: (12/37) wspeed (time) float64 ... T (depth, time) float64 ... salt (depth, time) float64 ... pden (depth, time) float64 ... u (depth, time) float64 nan nan nan nan ... nan nan nan v (depth, time) float64 nan nan nan nan ... nan nan nan ... ... N2T (depth, time) float64 nan nan ... 4.785e-07 5.266e-06 shred2 (depth, time) float64 nan nan nan nan ... nan nan nan Rig_T (depth, time) float64 nan nan nan nan ... nan nan nan Rig (depth, time) float64 nan nan nan nan ... nan nan nan n2s2pdf (enso_transition_phase, N2T_bins, S2_bins) float64 ... eps_ri (enso_transition_phase, stat, Rig_T_bins) float64 ... Attributes: readme: ['this file is compiled from a binary '\n 'Fortran fil... name: Tropic Heattropicheat- depth: 250
- time: 3776
- zeuc: 80
- enso_transition_phase: 1
- N2T_bins: 29
- S2_bins: 29
- stat: 3
- Rig_T_bins: 9
- depth(depth)float64-250.0 -249.0 -248.0 ... -2.0 -1.0
- axis :
- Z
- positive :
- up
array([-250., -249., -248., ..., -3., -2., -1.])
- time(time)datetime64[ns]1991-11-04T18:43:50 ... 1991-11-...
- axis :
- T
- standard_name :
- time
array(['1991-11-04T18:43:50.000000000', '1991-11-04T18:46:51.000000000', '1991-11-04T18:53:21.000000000', ..., '1991-11-24T22:51:04.000000000', '1991-11-24T22:57:34.000000000', '1991-11-24T23:04:04.000000000'], dtype='datetime64[ns]') - latitude()int640
- units :
- degrees_north
- standard_name :
- latitude
array(0)
- longitude()int64-140
- units :
- degrees_east
- standard_name :
- longitude
array(-140)
- eucmax(time)float64nan nan nan ... -108.0 -108.0
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
array([ nan, nan, nan, ..., -108., -108., -108.])
- mldT(time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
array([nan, nan, nan, ..., nan, nan, nan])
- dcl_mask(depth, time)boolFalse False False ... False False
- description :
- True when 5m below mldT and above eucmax.
array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]]) - enso_transition_phase(enso_transition_phase)<U4'none'
array(['none'], dtype='<U4')
- N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - S2_bins(S2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} S^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - bin_areas(N2T_bins, S2_bins)float640.01 0.01 0.01 ... 0.01 0.01 0.01
- long_name :
- log$_{10} 4N_T^2$
array([[0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, ... 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]]) - stat(stat)object'mean' 'std' 'count'
array(['mean', 'std', 'count'], dtype=object)
- Rig_T_bins(Rig_T_bins)object(-1.6, -1.4000000000000001] ... ...
- long_name :
- $Ri^g_T$
array([Interval(-1.6, -1.4000000000000001, closed='right'), Interval(-1.4000000000000001, -1.2000000000000002, closed='right'), Interval(-1.2000000000000002, -1.0000000000000002, closed='right'), Interval(-1.0000000000000002, -0.8000000000000003, closed='right'), Interval(-0.8000000000000003, -0.6000000000000003, closed='right'), Interval(-0.6000000000000003, -0.40000000000000036, closed='right'), Interval(-0.40000000000000036, -0.2000000000000004, closed='right'), Interval(-0.2000000000000004, -4.440892098500626e-16, closed='right'), Interval(-4.440892098500626e-16, 0.1999999999999995, closed='right')], dtype=object)
- chi(depth, time)float64...
- long_name :
- $χ$
- units :
- °C²/s
[944000 values with dtype=float64]
- pres(depth)float64...
- standard_name :
- sea_water_pressure
- units :
- dbar
[250 values with dtype=float64]
- salt(depth, time)float64...
- standard_name :
- sea_water_salinity
- units :
- psu
[944000 values with dtype=float64]
- pden(depth, time)float64...
- standard_name :
- sea_water_potential_density
[944000 values with dtype=float64]
- T(depth, time)float64...
- standard_name :
- sea_water_temperature
- units :
- celsius
[944000 values with dtype=float64]
- theta(depth, time)float64...
- standard_name :
- sea_water_potential_temperature
- units :
- celsius
[944000 values with dtype=float64]
- eps(depth, time)float64...
- long_name :
- $ε$
- units :
- W/kg
[944000 values with dtype=float64]
- EPSILON_clean(depth, time)float64...
[944000 values with dtype=float64]
- u(depth, time)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_x_velocity
array([[ nan, nan, nan, ..., 0.035526, 0.034717, 0.034762], [ nan, nan, nan, ..., 0.041579, 0.0405 , 0.040347], [ nan, nan, nan, ..., 0.047633, 0.046282, 0.045933], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]) - v(depth, time)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_y_velocity
array([[ nan, nan, nan, ..., 0.025063, 0.027463, 0.029415], [ nan, nan, nan, ..., 0.024451, 0.026839, 0.028796], [ nan, nan, nan, ..., 0.023839, 0.026215, 0.028178], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]) - dTdz(depth, time)float64...
- units :
- celsius
[944000 values with dtype=float64]
- N2(depth, time)float64...
[944000 values with dtype=float64]
- Jq(depth, time)float64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[944000 values with dtype=float64]
- dJdz(depth, time)float64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[944000 values with dtype=float64]
- dTdt(depth, time)float64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[944000 values with dtype=float64]
- mld(time)float64...
- long_name :
- MLD
- units :
- m
- description :
- Interpolate density to 1m grid. Search for min depth where |drho| > 0.005 and N2 > 1e-08
[3776 values with dtype=float64]
- gamma_n(time, depth)float64...
- standard_name :
- neutral_density
- units :
- kg/m3
- long_name :
- $γ_n$
[944000 values with dtype=float64]
- Jq_euc(time, zeuc)float64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[302080 values with dtype=float64]
- dJdz_euc(time, zeuc)float64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[302080 values with dtype=float64]
- dTdt_euc(time, zeuc)float64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[302080 values with dtype=float64]
- u_euc(time, zeuc)float64...
[302080 values with dtype=float64]
- count_Jq_euc(time, zeuc)int64...
- long_name :
- $J_q^ε$
- units :
- W/m²
[302080 values with dtype=int64]
- count_dJdz_euc(time, zeuc)int64...
- long_name :
- $∂J_q^ε/∂z$
- units :
- W/kg/m
[302080 values with dtype=int64]
- count_dTdt_euc(time, zeuc)int64...
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q^ε/∂z$
- units :
- °C/month
[302080 values with dtype=int64]
- count_u_euc(time, zeuc)int64...
[302080 values with dtype=int64]
- S2(depth, time)float64nan nan nan nan ... nan nan nan nan
array([[ nan, nan, nan, ..., 3.70210900e-05, 3.38255498e-05, 3.15813067e-05], [ nan, nan, nan, ..., 3.70210900e-05, 3.38255498e-05, 3.15813067e-05], [ nan, nan, nan, ..., 3.70210900e-05, 3.38255498e-05, 3.15813067e-05], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]) - densT(depth, time)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_potential_density
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - Tz(depth, time)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - Sz(depth, time)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - N2T(depth, time)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - shred2(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - Rig_T(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Ri^g_T$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - Rig(depth, time)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $Ri^g$
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - n2s2pdf(enso_transition_phase, N2T_bins, S2_bins)float640.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
- long_name :
- $P(S^2, 4N_T^2)$
array([[[0. , 0. , 0. , 0. , 0. , 0.00785978, 0. , 0.01571956, 0. , 0. , 0.01571956, 0.01571956, 0.00785978, 0.01571956, 0.00785978, 0. , 0.00785978, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0.00785978, 0.00785978, 0. , 0. , 0. , 0.02357934, 0.00785978, 0.02357934, 0.03929891, 0.01571956, 0.04715869, 0.04715869, 0.03143913, 0.03143913, 0.00785978, 0. , 0.00785978, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0.00785978, 0. , 0. , 0. , 0.03143913, 0. , 0.01571956, 0.06287825, 0.07859781, 0.03929891, 0.03143913, 0.03143913, 0.03143913, 0.03929891, 0.00785978, 0. , 0.00785978, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0.00785978, 0.03143913, 0. , 0. , 0.00785978, 0.03143913, 0.03929891, 0.06287825, 0.05501847, 0.10217716, ... 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0.00785978, 0.00785978, 0.02357934, 0.00785978, 0. , 0.03143913, 0.03143913, 0.01571956, 0. , 0.06287825, 0. , 0.00785978, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0.00785978, 0.00785978, 0. , 0. , 0. , 0. , 0. , 0. , 0.00785978, 0.01571956, 0.00785978, 0.06287825, 0. , 0.00785978, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.00785978, 0. , 0.00785978, 0.00785978, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]]]) - eps_ri(enso_transition_phase, stat, Rig_T_bins)float647.739e-07 6.653e-07 ... 4.668e+03
- long_name :
- $ε$
- units :
- W/kg
array([[[7.73859257e-07, 6.65309753e-07, 5.48888873e-07, 5.08024348e-07, 4.08245297e-07, 3.96648184e-07, 4.73734534e-07, 5.77911961e-07, 5.33822550e-07], [3.01548595e-06, 2.24114077e-06, 1.73817476e-06, 1.96601425e-06, 1.41096891e-06, 1.52441577e-06, 1.96645008e-06, 2.39132373e-06, 2.07788235e-06], [2.71400000e+03, 5.09400000e+03, 8.76700000e+03, 1.56620000e+04, 2.48800000e+04, 2.83470000e+04, 1.99130000e+04, 1.00000000e+04, 4.66800000e+03]]])
- starttime :
- ['' '' '' ... 'Time:22:51:04 328 ' 'Time:22:57:35 328 ' 'Time:23:04:04 328 ']
- endtime :
- ['' '' '' ... 'Time:22:54:39 328 ' 'Time:23:01:10 328 ' 'Time:23:07:49 328 ']
- readme :
- ['EPSILON_clean cleaned using tw91_eps_chi_sum1.mat ' '(all that is marked NaN or missed in tw91_eps_chi_sum1.mat ' 'is marked NaN in that field too) plus bad_drops.40, ' 'which contained contaminated casts, is used to mark bad EPSILON']
- name :
- TIWE
<xarray.DatasetView> Dimensions: (depth: 250, time: 3776, zeuc: 80, enso_transition_phase: 1, N2T_bins: 29, S2_bins: 29, stat: 3, Rig_T_bins: 9) Coordinates: (12/13) * depth (depth) float64 -250.0 -249.0 -248.0 ... -2.0 -1.0 * time (time) datetime64[ns] 1991-11-04T18:43:50 ... 1991... latitude int64 0 longitude int64 -140 eucmax (time) float64 nan nan nan ... -108.0 -108.0 -108.0 mldT (time) float64 nan nan nan nan ... nan nan nan nan ... ... * enso_transition_phase (enso_transition_phase) <U4 'none' * N2T_bins (N2T_bins) object [-5.0, -4.9) ... [-2.20000000000... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200000000000... bin_areas (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01 * stat (stat) object 'mean' 'std' 'count' * Rig_T_bins (Rig_T_bins) object (-1.6, -1.4000000000000001] ..... Dimensions without coordinates: zeuc Data variables: (12/35) chi (depth, time) float64 ... pres (depth) float64 ... salt (depth, time) float64 ... pden (depth, time) float64 ... T (depth, time) float64 ... theta (depth, time) float64 ... ... ... N2T (depth, time) float64 nan nan nan nan ... nan nan nan shred2 (depth, time) float64 nan nan nan nan ... nan nan nan Rig_T (depth, time) float64 nan nan nan nan ... nan nan nan Rig (depth, time) float64 nan nan nan nan ... nan nan nan n2s2pdf (enso_transition_phase, N2T_bins, S2_bins) float64 ... eps_ri (enso_transition_phase, stat, Rig_T_bins) float64 ... Attributes: starttime: ['' '' '' ... 'Time:22:51:04 328 ' 'Time:22:57:35 328 '\n... endtime: ['' '' '' ... 'Time:22:54:39 328 ' 'Time:23:01:10 328 '\n... readme: ['EPSILON_clean cleaned using tw91_eps_chi_sum1.mat ... name: TIWEtiwe
TAO#
tao_Ri = xr.load_dataarray(
"tao-hourly-Ri-seasonal-percentiles.nc"
).cf.guess_coord_axis()
%autoreload
with dask.config.set(scheduler="threads"):
tao_gridded = mixpods.load_tao()
tao_gridded
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "depth" starting at index 42. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "time" starting at index 199726. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
<xarray.Dataset>
Dimensions: (time: 212302, depth: 61, depthchi: 6)
Coordinates: (12/19)
deepest (time) float64 dask.array<chunksize=(212302,), meta=np.ndarray>
* depth (depth) float64 -300.0 -295.0 -290.0 ... -10.0 -5.0 0.0
eucmax (time) float64 dask.array<chunksize=(33130,), meta=np.ndarray>
latitude float32 0.0
longitude float32 -140.0
mld (time) float64 dask.array<chunksize=(212302,), meta=np.ndarray>
... ...
oni (time) float32 -0.9 -0.9 -0.9 -0.9 ... nan nan nan nan
en_mask (time) bool False False False ... False False False
ln_mask (time) bool True True True True ... False False False
warm_mask (time) bool True True True True ... True True True True
cool_mask (time) bool False False False ... False False False
enso_transition (time) <U12 'La-Nina warm' ... '____________'
Data variables: (12/39)
N2 (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
N2T (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
Ri (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
Rig_T (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
S (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
S2 (time, depth) float32 dask.array<chunksize=(33130, 61), meta=np.ndarray>
... ...
Rig (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
sst (time) float64 dask.array<chunksize=(33130,), meta=np.ndarray>
Tflx_dia_diff (time, depthchi) float64 nan nan nan nan ... nan nan nan
ν (time, depthchi) float64 dask.array<chunksize=(33130, 6), meta=np.ndarray>
Jb (time, depthchi) float64 dask.array<chunksize=(33130, 6), meta=np.ndarray>
Rif (time, depthchi) float64 dask.array<chunksize=(33130, 6), meta=np.ndarray>
Attributes:
CREATION_DATE: 23:26 24-FEB-2021
Data_Source: Global Tropical Moored Buoy Array Project O...
File_info: Contact: Dai.C.McClurg@noaa.gov
Request_for_acknowledgement: If you use these data in publications or pr...
_FillValue: 1.0000000409184788e+35
array: TAO/TRITON
missing_value: 1.0000000409184788e+35
platform_code: 0n165e
site_code: 0n165e
wmo_platform_code: 52321- time: 212302
- depth: 61
- depthchi: 6
- deepest(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
- description :
- Deepest depth with a valid observation
- units :
- m
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - depth(depth)float64-300.0 -295.0 -290.0 ... -5.0 0.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255., -250., -245., -240., -235., -230., -225., -220., -215., -210., -205., -200., -195., -190., -185., -180., -175., -170., -165., -160., -155., -150., -145., -140., -135., -130., -125., -120., -115., -110., -105., -100., -95., -90., -85., -80., -75., -70., -65., -60., -55., -50., -45., -40., -35., -30., -25., -20., -15., -10., -5., 0.]) - eucmax(time)float64dask.array<chunksize=(33130,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 17 graph layers Data type float64 numpy.ndarray - latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- mld(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
- long_name :
- $z_{MLD}$
- units :
- m
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(33130,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 20 graph layers Data type float64 numpy.ndarray - reference_pressure()int640
array(0)
- shallowest(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - time(time)datetime64[ns]1996-01-01 ... 2020-03-20T21:00:00
array(['1996-01-01T00:00:00.000000000', '1996-01-01T01:00:00.000000000', '1996-01-01T02:00:00.000000000', ..., '2020-03-20T19:00:00.000000000', '2020-03-20T20:00:00.000000000', '2020-03-20T21:00:00.000000000'], dtype='datetime64[ns]') - zeuc(depth, time)float64dask.array<chunksize=(42, 132856), meta=np.ndarray>
Array Chunk Bytes 98.80 MiB 42.57 MiB Shape (61, 212302) (42, 132856) Dask graph 4 chunks in 3 graph layers Data type float64 numpy.ndarray - depthchi(depthchi)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-89., -69., -59., -49., -39., -29.])
- dcl_mask(depth, time)booldask.array<chunksize=(61, 33130), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 12.35 MiB 5.82 MiB Shape (61, 212302) (61, 100000) Dask graph 3 chunks in 45 graph layers Data type bool numpy.ndarray - oni(time)float32-0.9 -0.9 -0.9 -0.9 ... nan nan nan
array([-0.9, -0.9, -0.9, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'La-Nina warm' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['La-Nina warm', 'La-Nina warm', 'La-Nina warm', ..., '____________', '____________', '____________'], dtype='<U12')
- N2(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $N^2$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - N2T(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $N^2_T$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - Ri(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $Ri_g$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - Rig_T(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 7 graph layers Data type float64 numpy.ndarray - S(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 41
- generic_name :
- sal
- long_name :
- SALINITY (PSU)
- name :
- S
- standard_name :
- sea_water_salinity
- units :
- PSU
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - S2(time, depth)float32dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $S^2$
Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray - T(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- FORTRAN_format :
- f10.2
- epic_code :
- 20
- generic_name :
- temp
- long_name :
- TEMPERATURE (C)
- name :
- T
- standard_name :
- sea_water_temperature
- units :
- C
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - dens(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $ρ$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - densT(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- description :
- density using T, S
- long_name :
- $ρ_T$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - lwnet(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1136
- generic_name :
- qln
- long_name :
- NET LONGWAVE RADIATION
- name :
- LWN
- units :
- W m-2
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - qlat(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 137
- generic_name :
- qlat
- long_name :
- LATENT HEAT FLUX
- name :
- QL
- units :
- W m-2
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - qnet(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 210
- generic_name :
- qtot
- long_name :
- TOTAL HEAT FLUX
- name :
- QT
- units :
- W/M**2
- standard_name :
- surface_downward_heat_flux_in_sea_water
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - qsen(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 138
- generic_name :
- qsen
- long_name :
- SENSIBLE HEAT FLUX
- name :
- QS
- units :
- W m-2
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - swnet(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1495
- generic_name :
- sw
- long_name :
- NET SHORTWAVE RADIATION
- name :
- SWN
- units :
- W/M**2
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tau(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray - taux(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
- standard_name :
- surface_downward_x_stress
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - tauy(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
- standard_name :
- surface_downward_y_stress
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - theta(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $θ$
- standard_name :
- sea_water_potential_temperature
- units :
- degC
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - u(time, depth)float32dask.array<chunksize=(33130, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1205
- generic_name :
- u
- long_name :
- u
- name :
- u
- standard_name :
- sea_water_x_velocity
- units :
- m/s
Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray - v(time, depth)float32dask.array<chunksize=(33130, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1206
- generic_name :
- v
- long_name :
- v
- name :
- v
- standard_name :
- sea_water_y_velocity
- units :
- m/s
Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray - wind_dir(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 410
- generic_name :
- long_name :
- WIND DIRECTION
- name :
- WD
- standard_name :
- wind_from_direction
- units :
- degrees
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - pressure(depth)float64301.9 296.8 291.8 ... 5.028 -0.0
- standard_name :
- sea_water_pressure
- units :
- dbar
array([301.87732362, 296.84242473, 291.80764803, 286.77299352, 281.73846121, 276.70405112, 271.66976325, 266.63559761, 261.60155422, 256.56763308, 251.5338342 , 246.5001576 , 241.46660329, 236.43317126, 231.39986155, 226.36667414, 221.33360906, 216.30066632, 211.26784592, 206.23514788, 201.2025722 , 196.17011889, 191.13778797, 186.10557945, 181.07349333, 176.04152963, 171.00968835, 165.97796951, 160.94637311, 155.91489917, 150.8835477 , 145.8523187 , 140.82121218, 135.79022817, 130.75936665, 125.72862766, 120.69801119, 115.66751726, 110.63714587, 105.60689704, 100.57677078, 95.54676709, 90.51688599, 85.48712749, 80.4574916 , 75.42797832, 70.39858766, 65.36931965, 60.34017428, 55.31115157, 50.28225153, 45.25347416, 40.22481948, 35.1962875 , 30.16787822, 25.13959167, 20.11142784, 15.08338675, 10.0554684 , 5.02767282, -0. ]) - SA(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- standard_name :
- sea_water_absolute_salinity
- units :
- g/kg
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 5 graph layers Data type float64 numpy.ndarray - CT(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- standard_name :
- sea_water_conservative_temperature
- units :
- degC
- reference_scale :
- ITS-90
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 9 graph layers Data type float64 numpy.ndarray - α(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- units :
- 1/K
- standard_name :
- sea_water_thermal_expansion_coefficient
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 10 graph layers Data type float64 numpy.ndarray - β(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- units :
- kg/g
- standard_name :
- sea_water_haline_contraction_coefficient
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 10 graph layers Data type float64 numpy.ndarray - Tz(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- ℃/m
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray - Sz(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- g/kg/m
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray - chi(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - KT(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_heat_diffusivity
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - eps(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - Jq(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - shred2(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray - Rig(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $Ri^g$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 7 graph layers Data type float64 numpy.ndarray - sst(time)float64dask.array<chunksize=(33130,), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- units :
- degC
Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 4 graph layers Data type float64 numpy.ndarray - Tflx_dia_diff(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - ν(time, depthchi)float64dask.array<chunksize=(33130, 6), meta=np.ndarray>
- standard_name :
- ocean_vertical_momentum_diffusivity
Array Chunk Bytes 9.72 MiB 4.58 MiB Shape (212302, 6) (100000, 6) Dask graph 3 chunks in 12 graph layers Data type float64 numpy.ndarray - Jb(time, depthchi)float64dask.array<chunksize=(33130, 6), meta=np.ndarray>
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
Array Chunk Bytes 9.72 MiB 4.58 MiB Shape (212302, 6) (100000, 6) Dask graph 3 chunks in 49 graph layers Data type float64 numpy.ndarray - Rif(time, depthchi)float64dask.array<chunksize=(33130, 6), meta=np.ndarray>
- standard_name :
- flux_richardson_number
Array Chunk Bytes 9.72 MiB 4.58 MiB Shape (212302, 6) (100000, 6) Dask graph 3 chunks in 52 graph layers Data type float64 numpy.ndarray
- depthPandasIndex
PandasIndex(Float64Index([-300.0, -295.0, -290.0, -285.0, -280.0, -275.0, -270.0, -265.0, -260.0, -255.0, -250.0, -245.0, -240.0, -235.0, -230.0, -225.0, -220.0, -215.0, -210.0, -205.0, -200.0, -195.0, -190.0, -185.0, -180.0, -175.0, -170.0, -165.0, -160.0, -155.0, -150.0, -145.0, -140.0, -135.0, -130.0, -125.0, -120.0, -115.0, -110.0, -105.0, -100.0, -95.0, -90.0, -85.0, -80.0, -75.0, -70.0, -65.0, -60.0, -55.0, -50.0, -45.0, -40.0, -35.0, -30.0, -25.0, -20.0, -15.0, -10.0, -5.0, 0.0], dtype='float64', name='depth')) - timePandasIndex
PandasIndex(DatetimeIndex(['1996-01-01 00:00:00', '1996-01-01 01:00:00', '1996-01-01 02:00:00', '1996-01-01 03:00:00', '1996-01-01 04:00:00', '1996-01-01 05:00:00', '1996-01-01 06:00:00', '1996-01-01 07:00:00', '1996-01-01 08:00:00', '1996-01-01 09:00:00', ... '2020-03-20 12:00:00', '2020-03-20 13:00:00', '2020-03-20 14:00:00', '2020-03-20 15:00:00', '2020-03-20 16:00:00', '2020-03-20 17:00:00', '2020-03-20 18:00:00', '2020-03-20 19:00:00', '2020-03-20 20:00:00', '2020-03-20 21:00:00'], dtype='datetime64[ns]', name='time', length=212302, freq=None)) - depthchiPandasIndex
PandasIndex(Float64Index([-89.0, -69.0, -59.0, -49.0, -39.0, -29.0], dtype='float64', name='depthchi'))
- CREATION_DATE :
- 23:26 24-FEB-2021
- Data_Source :
- Global Tropical Moored Buoy Array Project Office/NOAA/PMEL
- File_info :
- Contact: Dai.C.McClurg@noaa.gov
- Request_for_acknowledgement :
- If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115
- _FillValue :
- 1.0000000409184788e+35
- array :
- TAO/TRITON
- missing_value :
- 1.0000000409184788e+35
- platform_code :
- 0n165e
- site_code :
- 0n165e
- wmo_platform_code :
- 52321
np.log10(tao_gridded.eps).sel(time=slice("2005", "2015")).resample(
time="M"
).mean().hvplot.quadmesh(clim=(-7.5, -6))
sub.v.hvplot()
sub = tao_gridded.sel(time="2010-01")
t = sub.theta.hvplot.quadmesh(cmap="turbo_r")
dt = (
sub.theta - sub.theta.reset_coords(drop=True).cf.sel(Z=[0, -5]).cf.max("Z")
).hvplot.quadmesh(clim=(-0.15, 0.15), cmap="RdBu_r")
newmld = mixpods.get_mld_tao_theta(sub.theta.reset_coords(drop=True))
(
dt
* sub.reset_coords().mldT.hvplot.line(color="w", line_width=2)
* newmld.reset_coords(drop=True).hvplot.line(color="orange", line_width=1)
).opts(frame_width=1200)
(
tao_gridded.reset_coords().mldT.resample(time="5D").mean().hvplot.line()
* mixpods.get_mld_tao_theta(tao_gridded.reset_coords().theta)
.resample(time="5D")
.mean()
.hvplot.line()
)
tao_gridded.u.cf.plot()
tao_gridded.eucmax.plot()
[<matplotlib.lines.Line2D>]
MITgcm stations#
stations = pump.model.read_stations_20(stationdirname)
gcmeq = stations.sel(
longitude=[-155.025, -140.025, -125.025, -110.025], method="nearest"
)
# enso = pump.obs.make_enso_mask()
# mitgcm["enso"] = enso.reindex(time=mitgcm.time.data, method="nearest")
# gcmeq["eucmax"] = pump.calc.get_euc_max(gcmeq.u)
# pump.calc.calc_reduced_shear(gcmeq)
# oni = pump.obs.process_oni()
# gcmeq["enso_transition"] = mixpods.make_enso_transition_mask(oni).reindex(time=gcmeq.time.data, method="nearest")
mitgcm = gcmeq.sel(longitude=-140.025, method="nearest")
metrics = mixpods.normalize_z(pump.model.read_metrics(stationdirname), sort=True)
mitgcm = mixpods.normalize_z(mitgcm, sort=True)
mitgcm_grid = xgcm.Grid(
metrics.sel(latitude=mitgcm.latitude, longitude=mitgcm.longitude, method="nearest"),
coords=({"Z": {"center": "depth", "outer": "RF"}, "Y": {"center": "latitude"}}),
metrics={"Z": ("drF", "drC")},
periodic=False,
boundary="fill",
fill_value=np.nan,
)
mitgcm.theta.attrs["standard_name"] = "sea_water_potential_temperature"
mitgcm.salt.attrs["standard_name"] = "sea_water_salinity"
mitgcm["KPPviscAz"].attrs["standard_name"] = "ocean_vertical_viscosity"
mitgcm["KPPdiffKzT"].attrs["standard_name"] = "ocean_vertical_heat_diffusivity"
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
mitgcm = (
mixpods.prepare(mitgcm, grid=mitgcm_grid, oni=pump.obs.process_oni())
.sel(latitude=0, method="nearest")
.cf.sel(Z=slice(-250, 0))
)
mitgcm_grid
<xgcm.Grid>
Z Axis (not periodic, boundary='fill'):
* center depth --> outer
* outer RF --> center
Y Axis (not periodic, boundary='fill'):
* center latitude
mitgcm.u.cf.plot()
mitgcm.mldT.reset_coords(drop=True).cf.plot()
mitgcm.eucmax.reset_coords(drop=True).cf.plot()
[<matplotlib.lines.Line2D>]
mixpods.plot_n2s2pdf(mitgcm.n2s2pdf.sel(enso_transition_phase="none"))
<matplotlib.contour.QuadContourSet>
MOM6#
Generate kechunk JSONs#
catalog_sub = {k: v for k, v in catalog_dict.items() if k}
catalog_sub
{ 'baseline.001': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'), 'epbl': ('ePBL', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods'), 'kpp.lmd.002': ( 'KPP Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.002.mixpods' ), 'kpp.lmd.003': ( 'KPP Ri0=0.5, Ric=0.2,', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.003.mixpods' ), 'kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods' ), 'baseline.N150': ('baseline N=150', 'gmom.e23.GJRAv3.TL319_t061_zstar_N150.baseline.mixpods'), 'kpp.lmd.004.N150': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5, N=150', 'gmom.e23.GJRAv3.TL319_t061_zstar_N150.kpp.lmd.004.mixpods' ), 'baseline.hb': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.hb'), 'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'), 'new_baseline.kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods' ), 'new_baseline.kpp.lmd.005': ( 'KPP ν0=2.5, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods' ) }
%autoreload
from mom6_tools.kerchunk import generate_references_for_stream
for _, casename in tqdm.tqdm(catalog_sub.values()):
caseroot = f"{mixpods.ROOT}/cesm/{casename}"
if "N150" in casename:
continue
print(caseroot)
for stream in ["h", "hm", "hm.wci", "sfc"]:
generate_references_for_stream(
caseroot=caseroot,
stream=stream,
missing_stream="warn",
existing_output="overwrite",
)
../pump-catalog catalog with 4 dataset(s) from 4 asset(s):
| unique | |
|---|---|
| casename | 1 |
| stream | 4 |
| path | 4 |
| baseline | 1 |
| levels | 1 |
| frequency | 1 |
| variables | 75 |
| shortname | 1 |
| description | 1 |
| derived_variables | 0 |
0%| | 0/1 [00:00<?, ?it/s]
/glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods
/glade/u/home/dcherian/python/mom6-tools/mom6_tools/kerchunk.py:104: RuntimeWarning: No files found for caseroot: /glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods, stream: hm.wci
warnings.warn(f"No files found for caseroot: {caseroot}, stream: {stream}", RuntimeWarning)
100%|██████████| 1/1 [01:34<00:00, 94.96s/it]
import pathlib
root = pathlib.Path(
"/glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods"
)
%autoreload
from mom6_tools.kerchunk import combine_stream_jsons_as_groups
for casename in tqdm.tqdm(catalog_sub.df["casename"].unique()):
caseroot = f"{mixpods.ROOT}/cesm/{casename}"
combine_stream_jsons_as_groups(caseroot=caseroot, streams=None)
100%|██████████| 10/10 [00:13<00:00, 1.37s/it]
staticfile = (
"/glade/u/home/dcherian/campaign-oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb/"
"run/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb.mom6.static.nc"
)
import fsspec
from mom6_tools.kerchunk import open_references_as_xarray
fs = fsspec.filesystem(
"reference",
fo=f"{caseroot}/run/jsons/combined.json",
skip_instance_cache=True,
)
mapper = fs.get_mapper(root="")
import datatree
xr.open_dataset(mapper, engine="zarr", use_cftime=True, consolidated=False)
<xarray.Dataset>
Dimensions: ()
Data variables:
*empty*open_references_as_xarray(f"{caseroot}/run/jsons/combined.json")
<xarray.Dataset>
Dimensions: ()
Data variables:
*empty*calculate ONI#
(
oniobs.hvplot.line(x="time", label="obs")
* onimom6.hvplot.line(x="time", label="MOM6")
).opts(ylabel="ONI [°C]")
oniobs.enso_transition_mask.plot()
onimom6.enso_transition_mask.plot(color="r")
[<matplotlib.lines.Line2D>]
MOM6 sections#
Combine sections#
catalog
{ 'baseline': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'), 'epbl': ('ePBL', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods'), 'kpp.lmd.002': ( 'KPP Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.002.mixpods' ), 'kpp.lmd.003': ( 'KPP Ri0=0.5, Ric=0.2,', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.003.mixpods' ), 'kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods' ), 'baseline.N150': ('baseline N=150', 'gmom.e23.GJRAv3.TL319_t061_zstar_N150.baseline.mixpods'), 'kpp.lmd.004.N150': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5, N=150', 'gmom.e23.GJRAv3.TL319_t061_zstar_N150.kpp.lmd.004.mixpods' ), 'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'), 'new_baseline.kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods' ), 'new_baseline.kpp.lmd.005': ( 'KPP ν0=2.5, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods' ) }
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods"
ds = mixpods.read_mom6_sections(casename)
100%|██████████| 39/39 [02:32<00:00, 3.90s/it]
ds.drop_vars(
["average_DT", "average_T2", "average_T1", "time_bnds"], errors="ignore"
).chunk({"time": 24 * 365}).to_zarr(
f"/glade/scratch/dcherian/archive/{casename}/ocn/moorings/tao.zarr",
consolidated=True,
mode="w",
)
<xarray.backends.zarr.ZarrStore object at 0x2aafa7cb0c10>
reload = mixpods.load_mom6_sections(casename)
reload.uo.count().compute()
%autoreload
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods"
casename = mixpods.mom6_sections_to_zarr(casename)
100%|██████████| 33/33 [00:55<00:00, 1.69s/it]
%autoreload
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods"
casename = mixpods.mom6_sections_to_zarr(casename)
100%|██████████| 33/33 [00:59<00:00, 1.81s/it]
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N150.baseline.mixpods"
mixpods.mom6_sections_to_zarr(casename)
100%|██████████| 39/39 [00:29<00:00, 1.30it/s]
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods"
mixpods.mom6_sections_to_zarr(casename)
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods"
mixpods.mom6_sections_to_zarr(casename)
100%|██████████| 31/31 [01:50<00:00, 3.57s/it]
/glade/u/home/dcherian/pump/pump/mixpods.py:887: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar. This may lead to subtle errors in operations that depend on the length of time between dates.
mom6tao["time"] = mom6tao.indexes["time"].to_datetimeindex()
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.002.mixpods"
mixpods.mom6_sections_to_zarr(casename)
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.003.mixpods"
mixpods.mom6_sections_to_zarr(casename)
100%|██████████| 38/38 [02:07<00:00, 3.36s/it]
/glade/u/home/dcherian/pump/pump/mixpods.py:900: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar. This may lead to subtle errors in operations that depend on the length of time between dates.
mom6tao["time"] = mom6tao.indexes["time"].to_datetimeindex()
casename = "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods"
mixpods.mom6_sections_to_zarr(casename)
Read sections#
dask.config.set(scheduler=client)
m = mixpods.read_mom6_sections(
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods"
)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask_jobqueue/core.py:255: FutureWarning: job_extra has been renamed to job_extra_directives. You are still using it (even if only set to []; please also check config files). If you did not set job_extra_directives yet, job_extra will be respected for now, but it will be removed in a future release. If you already set job_extra_directives, job_extra is ignored and you can remove it.
warnings.warn(warn, FutureWarning)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask_jobqueue/core.py:274: FutureWarning: env_extra has been renamed to job_script_prologue. You are still using it (even if only set to []; please also check config files). If you did not set job_script_prologue yet, env_extra will be respected for now, but it will be removed in a future release. If you already set job_script_prologue, env_extra is ignored and you can remove it.
warnings.warn(warn, FutureWarning)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask_jobqueue/pbs.py:82: FutureWarning: project has been renamed to account as this kwarg was used wit -A option. You are still using it (please also check config files). If you did not set account yet, project will be respected for now, but it will be removed in a future release. If you already set account, project is ignored and you can remove it.
warnings.warn(warn, FutureWarning)
100%|██████████| 21/21 [02:14<00:00, 6.42s/it]
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/coding/times.py:360: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
sample = dates.ravel()[0]
/glade/u/home/dcherian/pump/pump/mixpods.py:847: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar. This may lead to subtle errors in operations that depend on the length of time between dates.
mom6tao["time"] = mom6tao.indexes["time"].to_datetimeindex()
/glade/u/home/dcherian/pump/pump/mixpods.py:859: UserWarning: Kv_v not present. Assuming equal to Kv_u
warnings.warn("Kv_v not present. Assuming equal to Kv_u")
m.drop_vars(["average_DT", "average_T1", "average_T2", "time_bnds"]).chunk(
{"time": 365 * 24}
).to_zarr(
"/glade/scratch/dcherian/archive/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/ocn/moorings/tao.zarr",
mode="w",
)
m.sel(yh=0, method="nearest")[["ePBL_h_ML", "mlotst"]].to_array().hvplot.line(
by="variable", x="time"
)
m.sel(yh=0, method="nearest")[["Kd_heat", "Kd_ePBL"]].to_array().hvplot.line(
by="variable", groupby="time", logy=True, ylim=(1e-6, 1e-1), xlim=(0, 500)
)
mom6140 = mixpods.load_mom6_sections(casename).load()
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yh', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yh', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yq', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/cf_xarray/accessor.py:1638: UserWarning: Variables {'areacello'} not found in object but are referred to in the CF attributes.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yh', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yq', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
warnings.warn(
mom6140.uo.cf.plot(robust=True)
mom6140.eucmax.cf.plot()
mom6140.mldT.cf.plot(lw=0.5, color="k")
mixpods.plot_n2s2pdf(mom6140.n2s2pdf.sel(enso_transition_phase="none"))
<matplotlib.contour.QuadContourSet>
Pick simulations#
Build dict#
import warnings
datasets = {
# "TAO": tao_gridded,
# "MITgcm": mitgcm,
}
catalog_sub = {
k: catalog_dict[k]
for k in catalog_dict.keys()
if (k == "kpp.lmd.004") or ("baseline" in k and "150" not in k)
}
display(catalog_sub)
{ 'baseline': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'), 'kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods' ), 'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'), 'new_baseline.kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods' ), 'new_baseline.kpp.lmd.005': ( 'KPP ν0=2.5, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods' ) }
%autoreload
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
warnings.simplefilter("ignore", category=FutureWarning)
for short_name, (long_name, folder) in tqdm.tqdm(catalog_sub.items()):
datasets.update(
{
short_name: mixpods.load_mom6_sections(folder).assign_attrs(
{"title": long_name}
)
}
)
100%|██████████| 5/5 [00:35<00:00, 7.16s/it]
datasets["TAO"] = DataTree(tao_gridded)
datasets["les"] = les["0.0.-140.oct.average.month"].to_dataset()
# Offset LES to work with slicing below
datasets["les"]["time"] = datasets["les"]["time"] + pd.Timedelta(days=25 * 365)
Build tree#
tree = DataTree.from_dict(datasets)
tree = tree.sel(time=slice("2000", "2017"))
ref = tree["TAO"].ds.reset_coords(drop=True).cf.sel(Z=slice(-120, None))
counts = np.minimum(ref["S2"].cf.count("Z"), ref["N2T"].cf.count("Z")).load()
def calc_histograms(ds):
ds = ds.copy()
ds["tao_mask"] = counts.reindex(time=ds.time, method="nearest") > 5
ds["tao_mask"].attrs = {
"description": "True when there are more than 5 5-m T, u, v in TAO dataset"
}
# ds = ds.where(ds.tao_mask)
return ds.update(mixpods.pdf_N2S2(ds))
tree = tree.map_over_subtree(calc_histograms)
if "les" in tree:
tree["les"] = tree["les"].isel(z=slice(-2))
tree["les"]["KT"].attrs["standard_name"] = "ocean_vertical_heat_diffusivity"
if "micro" in locals():
tree.update(micro)
tree
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- time: 149184
- zi: 37
- zl: 37
- nv: 2
- N2T_bins: 29
- S2_bins: 29
- enso_transition_phase: 7
- stat: 2
- N2_bins: 29
- Rig_T_bins: 9
- nv(nv)float641.0 2.0
- cartesian_axis :
- N
- long_name :
- vertex number
- units :
- none
array([1., 2.])
- time(time)datetime64[ns]2000-01-01T00:30:00 ... 2017-12-...
array(['2000-01-01T00:30:00.000000000', '2000-01-01T01:30:00.000000000', '2000-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- cartesian_axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(1080,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.14 MiB 68.44 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(1080,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.14 MiB 68.44 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 1080), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 5.26 MiB 316.52 kiB Shape (37, 149184) (37, 8760) Dask graph 18 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float32-1.318 -1.318 ... -0.04526 -0.04526
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([-1.317616, -1.317616, -1.317616, ..., -0.045255, -0.045255, -0.045255], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'La-Nina cool' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., '____________', '____________', '____________'], dtype='<U12') - N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - S2_bins(S2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - enso_transition_phase(enso_transition_phase)object'none' 'El-Nino cool' ... 'all'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm', '____________', 'all'], dtype=object) - stat(stat)object'mean' 'count'
array(['mean', 'count'], dtype=object)
- N2_bins(N2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- $N^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - bin_areas(N2T_bins, S2_bins)float640.01 0.01 0.01 ... 0.01 0.01 0.01
- long_name :
- log$_{10} 4N_T^2$
- units :
- s$^{-2}$
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- long_name :
- $Ri^g_T$
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- KPP_OBLdepth(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
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- time_avg_info :
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- units :
- meter
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- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
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Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- standard_name :
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Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
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Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $N^2$
- units :
- s$^{-2}$
Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 28 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
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Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 28 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 51 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
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Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 67 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $Ri^g$
Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 66 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
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- units :
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Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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- standard_name :
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Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 63 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(1080, 37), meta=np.ndarray>
- units :
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- long_name :
- $J_q^t$
Array Chunk Bytes 42.11 MiB 2.47 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 7 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(1080, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 21.06 MiB 1.24 MiB Shape (149184, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
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Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 72 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 122 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 31 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(1080, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
- Total diapycnal diffusivity for heat at interfaces
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- units :
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Array Chunk Bytes 21.06 MiB 1.20 MiB Shape (149184, 37) (8760, 36) Dask graph 36 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(1080,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 582.75 kiB 34.22 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - tao_mask(time)boolTrue True True ... True True True
- description :
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array([ True, True, True, ..., True, True, True])
- n2s2pdf(N2T_bins, S2_bins, enso_transition_phase)float64dask.array<chunksize=(29, 29, 1), meta=np.ndarray>
- long_name :
- $P(S^2, 4N_T^2)$
Array Chunk Bytes 45.99 kiB 32.85 kiB Shape (29, 29, 7) (29, 29, 5) Dask graph 3 chunks in 152 graph layers Data type float64 numpy.ndarray - eps_n2s2(stat, N2_bins, S2_bins, enso_transition_phase)float64dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 91.98 kiB 45.99 kiB Shape (2, 29, 29, 7) (1, 29, 29, 7) Dask graph 2 chunks in 248 graph layers Data type float64 numpy.ndarray - eps_ri(stat, Rig_T_bins, enso_transition_phase)float64dask.array<chunksize=(1, 9, 1), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 0.98 kiB 432 B Shape (2, 9, 7) (1, 9, 6) Dask graph 4 chunks in 228 graph layers Data type float64 numpy.ndarray
- title :
- baseline
<xarray.DatasetView> Dimensions: (time: 149184, zi: 37, zl: 37, nv: 2, N2T_bins: 29, S2_bins: 29, enso_transition_phase: 7, stat: 2, N2_bins: 29, Rig_T_bins: 9) Coordinates: (12/23) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2000-01-01T00:30:00 ... 2017... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200000000000... * enso_transition_phase (enso_transition_phase) object 'none' ... 'all' * stat (stat) object 'mean' 'count' * N2_bins (N2_bins) object [-5.0, -4.9) ... [-2.200000000000... bin_areas (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01 * Rig_T_bins (Rig_T_bins) object (0.025118864315095794, 0.03981... Data variables: (12/43) KPP_OBLdepth (time) float32 dask.array<chunksize=(149184,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(1080, 37), meta=np.ndarray> KPP_kheat (time, zi) float32 dask.array<chunksize=(1080, 37), meta=np.ndarray> Kd_heat (time, zi) float32 dask.array<chunksize=(1080, 37), meta=np.ndarray> Kv_u (time, zl) float32 dask.array<chunksize=(1080, 37), meta=np.ndarray> Kv_v (time, zl) float32 dask.array<chunksize=(1080, 37), meta=np.ndarray> ... ... Rif (time, zi) float32 dask.array<chunksize=(1080, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(1080,), meta=np.ndarray> tao_mask (time) bool True True True True ... True True True n2s2pdf (N2T_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(29, 29, 1), meta=np.ndarray> eps_n2s2 (stat, N2_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray> eps_ri (stat, Rig_T_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 9, 1), meta=np.ndarray> Attributes: title: baselinebaseline- time: 130968
- zi: 37
- zl: 37
- nv: 2
- N2T_bins: 29
- S2_bins: 29
- enso_transition_phase: 7
- stat: 2
- N2_bins: 29
- Rig_T_bins: 9
- nv(nv)float641.0 2.0
- cartesian_axis :
- N
- long_name :
- vertex number
- units :
- none
array([1., 2.])
- time(time)datetime64[ns]2003-01-07T00:30:00 ... 2017-12-...
array(['2003-01-07T00:30:00.000000000', '2003-01-07T01:30:00.000000000', '2003-01-07T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- cartesian_axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.62 MiB 316.52 kiB Shape (37, 130968) (37, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan ... 0.09026 0.09026
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([ nan, nan, nan, ..., 0.090263, 0.090263, 0.090263], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12') - N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - S2_bins(S2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - enso_transition_phase(enso_transition_phase)object'none' 'El-Nino cool' ... 'all'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm', '____________', 'all'], dtype=object) - stat(stat)object'mean' 'count'
array(['mean', 'count'], dtype=object)
- N2_bins(N2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - bin_areas(N2T_bins, S2_bins)float640.01 0.01 0.01 ... 0.01 0.01 0.01
- long_name :
- log$_{10} 4N_T^2$
- units :
- s$^{-2}$
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- long_name :
- $Ri^g_T$
array([Interval(0.025118864315095794, 0.03981071705534971, closed='right'), Interval(0.03981071705534971, 0.0630957344480193, closed='right'), Interval(0.0630957344480193, 0.09999999999999995, closed='right'), Interval(0.09999999999999995, 0.15848931924611126, closed='right'), Interval(0.15848931924611126, 0.25118864315095785, closed='right'), Interval(0.25118864315095785, 0.3981071705534969, closed='right'), Interval(0.3981071705534969, 0.6309573444801927, closed='right'), Interval(0.6309573444801927, 0.999999999999999, closed='right'), Interval(0.999999999999999, 1.5848931924611116, closed='right')], dtype=object)
- KPP_N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLT_temp_budget(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat content change due to non-local transport, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
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- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- standard_name :
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- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - S2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
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- long_name :
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- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - T_advection_xy(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Horizontal convergence of residual mean advective fluxes of heat
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - T_lbdxy_cont_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
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- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
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- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Th_tendency_vert_remap(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Vertical remapping tracer content tendency for Heat
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - boundary_forcing_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Boundary forcing heat tendency
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Sea Water Salinity
- standard_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
- Sea Water Potential Temperature
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - frazil_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat tendency due to frazil formation
- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Layer Thickness
- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
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- units :
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Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - opottempdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - opottemppmdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - opottemptend(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Tendency of Sea Water Potential Temperature Expressed as Heat Content
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- standard_name :
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- units :
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Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- units :
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Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
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Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
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- standard_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 36.97 MiB 2.47 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- interp_method :
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- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
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- standard_name :
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- units :
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Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
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- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 511.59 kiB 34.22 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - tao_mask(time)boolTrue True True ... True True True
- description :
- True when there are more than 5 5-m T, u, v in TAO dataset
array([ True, True, True, ..., True, True, True])
- n2s2pdf(N2T_bins, S2_bins, enso_transition_phase)float64dask.array<chunksize=(29, 29, 1), meta=np.ndarray>
- long_name :
- $P(S^2, 4N_T^2)$
Array Chunk Bytes 45.99 kiB 32.85 kiB Shape (29, 29, 7) (29, 29, 5) Dask graph 3 chunks in 151 graph layers Data type float64 numpy.ndarray - eps_n2s2(stat, N2_bins, S2_bins, enso_transition_phase)float64dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 91.98 kiB 45.99 kiB Shape (2, 29, 29, 7) (1, 29, 29, 7) Dask graph 2 chunks in 236 graph layers Data type float64 numpy.ndarray - eps_ri(stat, Rig_T_bins, enso_transition_phase)float64dask.array<chunksize=(1, 9, 1), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 0.98 kiB 432 B Shape (2, 9, 7) (1, 9, 6) Dask graph 4 chunks in 222 graph layers Data type float64 numpy.ndarray
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 130968, zi: 37, zl: 37, nv: 2, N2T_bins: 29, S2_bins: 29, enso_transition_phase: 7, stat: 2, N2_bins: 29, Rig_T_bins: 9) Coordinates: (12/23) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-07T00:30:00... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 ... -2.5 -0.0 ... ... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200... * enso_transition_phase (enso_transition_phase) object 'none' ...... * stat (stat) object 'mean' 'count' * N2_bins (N2_bins) object [-5.0, -4.9) ... [-2.200... bin_areas (N2T_bins, S2_bins) float64 0.01 ... 0.01 * Rig_T_bins (Rig_T_bins) object (0.025118864315095794... Data variables: (12/58) KPP_N2 (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLT_temp_budget (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(130968,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_kheat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> tao_mask (time) bool True True True ... True True n2s2pdf (N2T_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(29, 29, 1), meta=np.ndarray> eps_n2s2 (stat, N2_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray> eps_ri (stat, Rig_T_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 9, 1), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5kpp.lmd.004- time: 157680
- zl: 37
- zi: 37
- nv: 2
- N2T_bins: 29
- S2_bins: 29
- enso_transition_phase: 7
- stat: 2
- N2_bins: 29
- Rig_T_bins: 9
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2000-01-01T00:30:00 ... 2017-12-...
array(['2000-01-01T00:30:00.000000000', '2000-01-01T01:30:00.000000000', '2000-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.20 MiB 68.44 kiB Shape (157680,) (8760,) Dask graph 18 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.20 MiB 68.44 kiB Shape (157680,) (8760,) Dask graph 18 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 5.56 MiB 316.52 kiB Shape (37, 157680) (37, 8760) Dask graph 18 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float32-1.277 -1.277 -1.277 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([-1.277248, -1.277248, -1.277248, ..., nan, nan, nan], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'La-Nina cool' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12') - N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - S2_bins(S2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - enso_transition_phase(enso_transition_phase)object'none' 'El-Nino cool' ... 'all'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm', '____________', 'all'], dtype=object) - stat(stat)object'mean' 'count'
array(['mean', 'count'], dtype=object)
- N2_bins(N2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - bin_areas(N2T_bins, S2_bins)float640.01 0.01 0.01 ... 0.01 0.01 0.01
- long_name :
- log$_{10} 4N_T^2$
- units :
- s$^{-2}$
array([[0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, ... 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]]) - Rig_T_bins(Rig_T_bins)object(0.025118864315095794, 0.0398107...
- long_name :
- $Ri^g_T$
array([Interval(0.025118864315095794, 0.03981071705534971, closed='right'), Interval(0.03981071705534971, 0.0630957344480193, closed='right'), Interval(0.0630957344480193, 0.09999999999999995, closed='right'), Interval(0.09999999999999995, 0.15848931924611126, closed='right'), Interval(0.15848931924611126, 0.25118864315095785, closed='right'), Interval(0.25118864315095785, 0.3981071705534969, closed='right'), Interval(0.3981071705534969, 0.6309573444801927, closed='right'), Interval(0.6309573444801927, 0.999999999999999, closed='right'), Interval(0.999999999999999, 1.5848931924611116, closed='right')], dtype=object)
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- units :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- cell_measures :
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- units :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
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- cell_measures :
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- units :
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- Layer Thickness
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
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- long_name :
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- units :
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.20 MiB Shape (157680, 37) (8760, 36) Dask graph 36 chunks in 56 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.24 MiB Shape (157680, 37) (8760, 37) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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Array Chunk Bytes 22.26 MiB 1.20 MiB Shape (157680, 37) (8760, 36) Dask graph 36 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
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array([ True, True, True, ..., True, True, True])
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Array Chunk Bytes 0.98 kiB 432 B Shape (2, 9, 7) (1, 9, 6) Dask graph 4 chunks in 228 graph layers Data type float64 numpy.ndarray
- title :
- KD=0, KV=0
<xarray.DatasetView> Dimensions: (time: 157680, zl: 37, zi: 37, nv: 2, N2T_bins: 29, S2_bins: 29, enso_transition_phase: 7, stat: 2, N2_bins: 29, Rig_T_bins: 9) Coordinates: (12/23) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2000-01-01T00:30:00 ... 2017... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200000000000... * enso_transition_phase (enso_transition_phase) object 'none' ... 'all' * stat (stat) object 'mean' 'count' * N2_bins (N2_bins) object [-5.0, -4.9) ... [-2.200000000000... bin_areas (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01 * Rig_T_bins (Rig_T_bins) object (0.025118864315095794, 0.03981... Data variables: (12/53) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(157680,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(157680,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> tao_mask (time) bool True True True True ... True True True n2s2pdf (N2T_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(29, 29, 1), meta=np.ndarray> eps_n2s2 (stat, N2_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray> eps_ri (stat, Rig_T_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 9, 1), meta=np.ndarray> Attributes: title: KD=0, KV=0new_baseline.hb- time: 113880
- zl: 37
- zi: 37
- nv: 2
- N2T_bins: 29
- S2_bins: 29
- enso_transition_phase: 7
- stat: 2
- N2_bins: 29
- Rig_T_bins: 9
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2015-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2015-12-31T21:30:00.000000000', '2015-12-31T22:30:00.000000000', '2015-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.02 MiB 316.52 kiB Shape (37, 113880) (37, 8760) Dask graph 13 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan nan ... nan nan nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12') - N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - S2_bins(S2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - enso_transition_phase(enso_transition_phase)object'none' 'El-Nino cool' ... 'all'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm', '____________', 'all'], dtype=object) - stat(stat)object'mean' 'count'
array(['mean', 'count'], dtype=object)
- N2_bins(N2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - bin_areas(N2T_bins, S2_bins)float640.01 0.01 0.01 ... 0.01 0.01 0.01
- long_name :
- log$_{10} 4N_T^2$
- units :
- s$^{-2}$
array([[0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, ... 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]]) - Rig_T_bins(Rig_T_bins)object(0.025118864315095794, 0.0398107...
- long_name :
- $Ri^g_T$
array([Interval(0.025118864315095794, 0.03981071705534971, closed='right'), Interval(0.03981071705534971, 0.0630957344480193, closed='right'), Interval(0.0630957344480193, 0.09999999999999995, closed='right'), Interval(0.09999999999999995, 0.15848931924611126, closed='right'), Interval(0.15848931924611126, 0.25118864315095785, closed='right'), Interval(0.25118864315095785, 0.3981071705534969, closed='right'), Interval(0.3981071705534969, 0.6309573444801927, closed='right'), Interval(0.6309573444801927, 0.999999999999999, closed='right'), Interval(0.999999999999999, 1.5848931924611116, closed='right')], dtype=object)
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
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- units :
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Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- standard_name :
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- units :
- m2 s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- units :
- m2 s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
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- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
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- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
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- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 32.15 MiB 2.47 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.07 MiB 1.24 MiB Shape (113880, 37) (8760, 37) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- W/kg
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- C^2/s
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.07 MiB 1.20 MiB Shape (113880, 37) (8760, 36) Dask graph 26 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 444.84 kiB 34.22 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - tao_mask(time)boolTrue True True ... True True True
- description :
- True when there are more than 5 5-m T, u, v in TAO dataset
array([ True, True, True, ..., True, True, True])
- n2s2pdf(N2T_bins, S2_bins, enso_transition_phase)float64dask.array<chunksize=(29, 29, 1), meta=np.ndarray>
- long_name :
- $P(S^2, 4N_T^2)$
Array Chunk Bytes 45.99 kiB 32.85 kiB Shape (29, 29, 7) (29, 29, 5) Dask graph 3 chunks in 148 graph layers Data type float64 numpy.ndarray - eps_n2s2(stat, N2_bins, S2_bins, enso_transition_phase)float64dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 91.98 kiB 45.99 kiB Shape (2, 29, 29, 7) (1, 29, 29, 7) Dask graph 2 chunks in 232 graph layers Data type float64 numpy.ndarray - eps_ri(stat, Rig_T_bins, enso_transition_phase)float64dask.array<chunksize=(1, 9, 1), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 0.98 kiB 432 B Shape (2, 9, 7) (1, 9, 6) Dask graph 4 chunks in 220 graph layers Data type float64 numpy.ndarray
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 113880, zl: 37, zi: 37, nv: 2, N2T_bins: 29, S2_bins: 29, enso_transition_phase: 7, stat: 2, N2_bins: 29, Rig_T_bins: 9) Coordinates: (12/23) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2015... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200000000000... * enso_transition_phase (enso_transition_phase) object 'none' ... 'all' * stat (stat) object 'mean' 'count' * N2_bins (N2_bins) object [-5.0, -4.9) ... [-2.200000000000... bin_areas (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01 * Rig_T_bins (Rig_T_bins) object (0.025118864315095794, 0.03981... Data variables: (12/53) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> tao_mask (time) bool True True True True ... True True True n2s2pdf (N2T_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(29, 29, 1), meta=np.ndarray> eps_n2s2 (stat, N2_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray> eps_ri (stat, Rig_T_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 9, 1), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5new_baseline.kpp.lmd.004- time: 105120
- zl: 37
- zi: 37
- nv: 2
- N2T_bins: 29
- S2_bins: 29
- enso_transition_phase: 7
- stat: 2
- N2_bins: 29
- Rig_T_bins: 9
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2014-12-...
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- long_name :
- h point nominal latitude
- units :
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array(0.06249997)
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- axis :
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- long_name :
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- axis :
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- long_name :
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- positive :
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- units :
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- axis :
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- long_name :
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- units :
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- long_name :
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- positive :
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- long_name :
- MLD$_θ$
- units :
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- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
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- description :
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- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
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- average_T1,average_T2,average_DT
- units :
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array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)
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- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12') - N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
- units :
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- units :
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array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm', '____________', 'all'], dtype=object) - stat(stat)object'mean' 'count'
array(['mean', 'count'], dtype=object)
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- cell_measures :
- area: areacello
- cell_methods :
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- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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- long_name :
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- units :
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- long_name :
- $Ri^g_T$
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- cell_measures :
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- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
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- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
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- units :
- meter
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
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- cell_measures :
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- cell_methods :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
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- cell_measures :
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- cell_methods :
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- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- interp_method :
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- time_avg_info :
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- time_avg_info :
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- units :
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- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
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- units :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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- units :
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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- units :
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- units :
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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- units :
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- time_avg_info :
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- units :
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Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
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Array Chunk Bytes 29.67 MiB 2.47 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- units :
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Array Chunk Bytes 14.84 MiB 1.24 MiB Shape (105120, 37) (8760, 37) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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Array Chunk Bytes 14.84 MiB 1.20 MiB Shape (105120, 37) (8760, 36) Dask graph 24 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
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- units :
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- description :
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- units :
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- units :
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Array Chunk Bytes 0.98 kiB 432 B Shape (2, 9, 7) (1, 9, 6) Dask graph 4 chunks in 220 graph layers Data type float64 numpy.ndarray
- title :
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<xarray.DatasetView> Dimensions: (time: 105120, zl: 37, zi: 37, nv: 2, N2T_bins: 29, S2_bins: 29, enso_transition_phase: 7, stat: 2, N2_bins: 29, Rig_T_bins: 9) Coordinates: (12/23) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2014... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200000000000... * enso_transition_phase (enso_transition_phase) object 'none' ... 'all' * stat (stat) object 'mean' 'count' * N2_bins (N2_bins) object [-5.0, -4.9) ... [-2.200000000000... bin_areas (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01 * Rig_T_bins (Rig_T_bins) object (0.025118864315095794, 0.03981... Data variables: (12/53) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> tao_mask (time) bool True True True True ... True True True n2s2pdf (N2T_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(29, 29, 1), meta=np.ndarray> eps_n2s2 (stat, N2_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray> eps_ri (stat, Rig_T_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 9, 1), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ri0=0.5new_baseline.kpp.lmd.005- time: 157800
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Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(98066,), meta=np.ndarray>
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Array Chunk Bytes 1.20 MiB 766.14 kiB Shape (157800,) (98066,) Dask graph 2 chunks in 21 graph layers Data type float64 numpy.ndarray - reference_pressure()int640
array(0)
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Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - time(time)datetime64[ns]2000-01-01 ... 2017-12-31T23:00:00
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- axis :
- Z
- positive :
- up
- units :
- m
array([-89., -69., -59., -49., -39., -29.])
- dcl_mask(depth, time)booldask.array<chunksize=(61, 98066), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 9.18 MiB 5.70 MiB Shape (61, 157800) (61, 98066) Dask graph 2 chunks in 46 graph layers Data type bool numpy.ndarray - oni(time)float32-1.66 -1.66 -1.66 ... -0.87 -0.87
array([-1.66, -1.66, -1.66, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'La-Nina cool' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12') - N2T_bins(N2T_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- log$_{10} 4N_T^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - S2_bins(S2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- $S^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - enso_transition_phase(enso_transition_phase)object'none' 'El-Nino cool' ... 'all'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm', '____________', 'all'], dtype=object) - stat(stat)object'mean' 'count'
array(['mean', 'count'], dtype=object)
- N2_bins(N2_bins)object[-5.0, -4.9) ... [-2.20000000000...
- long_name :
- $N^2$
array([Interval(-5.0, -4.9, closed='left'), Interval(-4.9, -4.800000000000001, closed='left'), Interval(-4.800000000000001, -4.700000000000001, closed='left'), Interval(-4.700000000000001, -4.600000000000001, closed='left'), Interval(-4.600000000000001, -4.500000000000002, closed='left'), Interval(-4.500000000000002, -4.400000000000002, closed='left'), Interval(-4.400000000000002, -4.3000000000000025, closed='left'), Interval(-4.3000000000000025, -4.200000000000003, closed='left'), Interval(-4.200000000000003, -4.100000000000003, closed='left'), Interval(-4.100000000000003, -4.0000000000000036, closed='left'), Interval(-4.0000000000000036, -3.900000000000004, closed='left'), Interval(-3.900000000000004, -3.8000000000000043, closed='left'), Interval(-3.8000000000000043, -3.7000000000000046, closed='left'), Interval(-3.7000000000000046, -3.600000000000005, closed='left'), Interval(-3.600000000000005, -3.5000000000000053, closed='left'), Interval(-3.5000000000000053, -3.4000000000000057, closed='left'), Interval(-3.4000000000000057, -3.300000000000006, closed='left'), Interval(-3.300000000000006, -3.2000000000000064, closed='left'), Interval(-3.2000000000000064, -3.1000000000000068, closed='left'), Interval(-3.1000000000000068, -3.000000000000007, closed='left'), Interval(-3.000000000000007, -2.9000000000000075, closed='left'), Interval(-2.9000000000000075, -2.800000000000008, closed='left'), Interval(-2.800000000000008, -2.700000000000008, closed='left'), Interval(-2.700000000000008, -2.6000000000000085, closed='left'), Interval(-2.6000000000000085, -2.500000000000009, closed='left'), Interval(-2.500000000000009, -2.4000000000000092, closed='left'), Interval(-2.4000000000000092, -2.3000000000000096, closed='left'), Interval(-2.3000000000000096, -2.20000000000001, closed='left'), Interval(-2.20000000000001, -2.1000000000000103, closed='left')], dtype=object) - bin_areas(N2T_bins, S2_bins)float640.01 0.01 0.01 ... 0.01 0.01 0.01
- long_name :
- log$_{10} 4N_T^2$
array([[0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, ... 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]]) - Rig_T_bins(Rig_T_bins)object(0.025118864315095794, 0.0398107...
- long_name :
- $Ri^g_T$
array([Interval(0.025118864315095794, 0.03981071705534971, closed='right'), Interval(0.03981071705534971, 0.0630957344480193, closed='right'), Interval(0.0630957344480193, 0.09999999999999995, closed='right'), Interval(0.09999999999999995, 0.15848931924611126, closed='right'), Interval(0.15848931924611126, 0.25118864315095785, closed='right'), Interval(0.25118864315095785, 0.3981071705534969, closed='right'), Interval(0.3981071705534969, 0.6309573444801927, closed='right'), Interval(0.6309573444801927, 0.999999999999999, closed='right'), Interval(0.999999999999999, 1.5848931924611116, closed='right')], dtype=object)
- N2(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $N^2$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - N2T(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $N^2_T$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - Ri(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $Ri_g$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - Rig_T(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray - S(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 41
- generic_name :
- sal
- long_name :
- SALINITY (PSU)
- name :
- S
- standard_name :
- sea_water_salinity
- units :
- PSU
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - S2(time, depth)float32dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $S^2$
Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - T(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- FORTRAN_format :
- f10.2
- epic_code :
- 20
- generic_name :
- temp
- long_name :
- TEMPERATURE (C)
- name :
- T
- standard_name :
- sea_water_temperature
- units :
- C
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - dens(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $ρ$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - densT(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- description :
- density using T, S
- long_name :
- $ρ_T$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - lwnet(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1136
- generic_name :
- qln
- long_name :
- NET LONGWAVE RADIATION
- name :
- LWN
- units :
- W m-2
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qlat(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 137
- generic_name :
- qlat
- long_name :
- LATENT HEAT FLUX
- name :
- QL
- units :
- W m-2
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qnet(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 210
- generic_name :
- qtot
- long_name :
- TOTAL HEAT FLUX
- name :
- QT
- units :
- W/M**2
- standard_name :
- surface_downward_heat_flux_in_sea_water
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qsen(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 138
- generic_name :
- qsen
- long_name :
- SENSIBLE HEAT FLUX
- name :
- QS
- units :
- W m-2
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - swnet(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1495
- generic_name :
- sw
- long_name :
- NET SHORTWAVE RADIATION
- name :
- SWN
- units :
- W/M**2
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tau(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray - taux(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
- standard_name :
- surface_downward_x_stress
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - tauy(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
- standard_name :
- surface_downward_y_stress
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - theta(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $θ$
- standard_name :
- sea_water_potential_temperature
- units :
- degC
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - u(time, depth)float32dask.array<chunksize=(98066, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1205
- generic_name :
- u
- long_name :
- u
- name :
- u
- standard_name :
- sea_water_x_velocity
- units :
- m/s
Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - v(time, depth)float32dask.array<chunksize=(98066, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1206
- generic_name :
- v
- long_name :
- v
- name :
- v
- standard_name :
- sea_water_y_velocity
- units :
- m/s
Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - wind_dir(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 410
- generic_name :
- long_name :
- WIND DIRECTION
- name :
- WD
- standard_name :
- wind_from_direction
- units :
- degrees
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - pressure(depth)float64301.9 296.8 291.8 ... 5.028 -0.0
- standard_name :
- sea_water_pressure
- units :
- dbar
array([301.87732362, 296.84242473, 291.80764803, 286.77299352, 281.73846121, 276.70405112, 271.66976325, 266.63559761, 261.60155422, 256.56763308, 251.5338342 , 246.5001576 , 241.46660329, 236.43317126, 231.39986155, 226.36667414, 221.33360906, 216.30066632, 211.26784592, 206.23514788, 201.2025722 , 196.17011889, 191.13778797, 186.10557945, 181.07349333, 176.04152963, 171.00968835, 165.97796951, 160.94637311, 155.91489917, 150.8835477 , 145.8523187 , 140.82121218, 135.79022817, 130.75936665, 125.72862766, 120.69801119, 115.66751726, 110.63714587, 105.60689704, 100.57677078, 95.54676709, 90.51688599, 85.48712749, 80.4574916 , 75.42797832, 70.39858766, 65.36931965, 60.34017428, 55.31115157, 50.28225153, 45.25347416, 40.22481948, 35.1962875 , 30.16787822, 25.13959167, 20.11142784, 15.08338675, 10.0554684 , 5.02767282, -0. ]) - SA(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- standard_name :
- sea_water_absolute_salinity
- units :
- g/kg
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 6 graph layers Data type float64 numpy.ndarray - CT(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- standard_name :
- sea_water_conservative_temperature
- units :
- degC
- reference_scale :
- ITS-90
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 10 graph layers Data type float64 numpy.ndarray - α(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- units :
- 1/K
- standard_name :
- sea_water_thermal_expansion_coefficient
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray - β(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- units :
- kg/g
- standard_name :
- sea_water_haline_contraction_coefficient
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray - Tz(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- ℃/m
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - Sz(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- g/kg/m
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - chi(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - KT(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_heat_diffusivity
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - eps(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - Jq(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - shred2(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - Rig(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $Ri^g$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray - sst(time)float64dask.array<chunksize=(98066,), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- units :
- degC
Array Chunk Bytes 1.20 MiB 766.14 kiB Shape (157800,) (98066,) Dask graph 2 chunks in 5 graph layers Data type float64 numpy.ndarray - Tflx_dia_diff(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - ν(time, depthchi)float64dask.array<chunksize=(98066, 6), meta=np.ndarray>
- standard_name :
- ocean_vertical_momentum_diffusivity
Array Chunk Bytes 7.22 MiB 4.49 MiB Shape (157800, 6) (98066, 6) Dask graph 2 chunks in 13 graph layers Data type float64 numpy.ndarray - Jb(time, depthchi)float64dask.array<chunksize=(98066, 6), meta=np.ndarray>
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
Array Chunk Bytes 7.22 MiB 4.49 MiB Shape (157800, 6) (98066, 6) Dask graph 2 chunks in 50 graph layers Data type float64 numpy.ndarray - Rif(time, depthchi)float64dask.array<chunksize=(98066, 6), meta=np.ndarray>
- standard_name :
- flux_richardson_number
Array Chunk Bytes 7.22 MiB 4.49 MiB Shape (157800, 6) (98066, 6) Dask graph 2 chunks in 53 graph layers Data type float64 numpy.ndarray - tao_mask(time)boolTrue True True ... True True True
- description :
- True when there are more than 5 5-m T, u, v in TAO dataset
array([ True, True, True, ..., True, True, True])
- n2s2pdf(N2T_bins, S2_bins, enso_transition_phase)float64dask.array<chunksize=(29, 29, 1), meta=np.ndarray>
- long_name :
- $P(S^2, 4N_T^2)$
Array Chunk Bytes 45.99 kiB 32.85 kiB Shape (29, 29, 7) (29, 29, 5) Dask graph 3 chunks in 82 graph layers Data type float64 numpy.ndarray - eps_n2s2(stat, N2_bins, S2_bins, enso_transition_phase)float64dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray>
Array Chunk Bytes 91.98 kiB 45.99 kiB Shape (2, 29, 29, 7) (1, 29, 29, 7) Dask graph 2 chunks in 92 graph layers Data type float64 numpy.ndarray - eps_ri(stat, Rig_T_bins, enso_transition_phase)float64dask.array<chunksize=(1, 9, 1), meta=np.ndarray>
Array Chunk Bytes 0.98 kiB 432 B Shape (2, 9, 7) (1, 9, 6) Dask graph 4 chunks in 60 graph layers Data type float64 numpy.ndarray
- CREATION_DATE :
- 23:26 24-FEB-2021
- Data_Source :
- Global Tropical Moored Buoy Array Project Office/NOAA/PMEL
- File_info :
- Contact: Dai.C.McClurg@noaa.gov
- Request_for_acknowledgement :
- If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115
- _FillValue :
- 1.0000000409184788e+35
- array :
- TAO/TRITON
- missing_value :
- 1.0000000409184788e+35
- platform_code :
- 0n165e
- site_code :
- 0n165e
- wmo_platform_code :
- 52321
<xarray.DatasetView> Dimensions: (time: 157800, depth: 61, depthchi: 6, N2T_bins: 29, S2_bins: 29, enso_transition_phase: 7, stat: 2, N2_bins: 29, Rig_T_bins: 9) Coordinates: (12/26) deepest (time) float64 dask.array<chunksize=(157800,), meta=np.ndarray> * depth (depth) float64 -300.0 -295.0 -290.0 ... -5.0 0.0 eucmax (time) float64 dask.array<chunksize=(98066,), meta=np.ndarray> latitude float32 0.0 longitude float32 -140.0 mld (time) float64 dask.array<chunksize=(157800,), meta=np.ndarray> ... ... * S2_bins (S2_bins) object [-5.0, -4.9) ... [-2.200000000000... * enso_transition_phase (enso_transition_phase) object 'none' ... 'all' * stat (stat) object 'mean' 'count' * N2_bins (N2_bins) object [-5.0, -4.9) ... [-2.200000000000... bin_areas (N2T_bins, S2_bins) float64 0.01 0.01 ... 0.01 0.01 * Rig_T_bins (Rig_T_bins) object (0.025118864315095794, 0.03981... Data variables: (12/43) N2 (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> N2T (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> Ri (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> Rig_T (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> S (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> S2 (time, depth) float32 dask.array<chunksize=(98066, 61), meta=np.ndarray> ... ... Jb (time, depthchi) float64 dask.array<chunksize=(98066, 6), meta=np.ndarray> Rif (time, depthchi) float64 dask.array<chunksize=(98066, 6), meta=np.ndarray> tao_mask (time) bool True True True True ... True True True n2s2pdf (N2T_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(29, 29, 1), meta=np.ndarray> eps_n2s2 (stat, N2_bins, S2_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 29, 29, 7), meta=np.ndarray> eps_ri (stat, Rig_T_bins, enso_transition_phase) float64 dask.array<chunksize=(1, 9, 1), meta=np.ndarray> Attributes: CREATION_DATE: 23:26 24-FEB-2021 Data_Source: Global Tropical Moored Buoy Array Project O... File_info: Contact: Dai.C.McClurg@noaa.gov Request_for_acknowledgement: If you use these data in publications or pr... _FillValue: 1.0000000409184788e+35 array: TAO/TRITON missing_value: 1.0000000409184788e+35 platform_code: 0n165e site_code: 0n165e wmo_platform_code: 52321TAO
%autoreload
dailies = tree.map_over_subtree(mixpods.daily_composites)
dailies
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered
r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-89., -69., -59., -49., -39., -29.])
- latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- reference_pressure()int640
array(0)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float649.959e-06 1.107e-05 ... 0.0001646
- standard_name :
- ocean_vertical_heat_diffusivity
array([[[9.95869314e-06, 1.10713814e-05, 1.81385305e-05], [7.62679606e-06, 9.84649659e-06, 1.98903482e-05], [8.26769194e-06, 1.03487366e-05, 1.69229065e-05], [5.09341517e-06, 1.06415264e-05, 9.94711354e-06], [6.27289392e-06, 1.01286448e-05, 9.09556433e-06], [5.85044689e-06, 9.09517149e-06, 1.06714061e-05], [5.92212630e-06, 8.40077865e-06, 1.11052356e-05], [6.47377658e-06, 9.78053302e-06, 1.13559910e-05], [5.77007820e-06, 1.00437481e-05, 1.54385405e-05], [5.60030739e-06, 8.32575368e-06, 1.31285365e-05], [5.27227053e-06, 7.15577324e-06, 1.44267402e-05], [5.03200713e-06, 9.93609875e-06, 1.68541630e-05], [6.42531180e-06, 1.04701955e-05, 1.45938509e-05], [5.45290240e-06, 1.06380336e-05, 1.94790247e-05], [5.05695470e-06, 1.08870254e-05, 2.13555496e-05], [6.78671892e-06, 1.23528905e-05, 2.01087241e-05], [9.80084606e-06, 1.38124457e-05, 2.85315836e-05], [6.90458131e-06, 1.42042099e-05, 2.59700678e-05], [8.44895131e-06, 1.38211283e-05, 2.92244979e-05], [6.96328801e-06, 1.80749482e-05, 2.81233184e-05], ... [2.49796366e-05, 1.51222154e-04, 1.54133302e-03], [2.54375198e-05, 1.92556637e-04, 1.98323078e-03], [2.95016029e-05, 2.74405783e-04, 2.23706350e-03], [2.91381387e-05, 5.29042782e-04, 2.46231496e-03], [3.22679193e-05, 6.25229486e-04, 2.36063586e-03], [4.87863828e-05, 6.82247064e-04, 2.44611497e-03], [7.65505437e-05, 8.72437582e-04, 2.08043411e-03], [9.60910794e-05, 7.32102430e-04, 2.33006858e-03], [8.45055247e-05, 8.06508643e-04, 1.81524742e-03], [9.68881747e-05, 7.19295971e-04, 2.07390795e-03], [1.12205990e-04, 8.31837440e-04, 1.82439093e-03], [1.25092879e-04, 8.00568096e-04, 1.85697065e-03], [1.41021393e-04, 7.84944210e-04, 1.71427588e-03], [1.35184262e-04, 7.04609354e-04, 1.41404584e-03], [1.12211968e-04, 3.97215676e-04, 1.07973119e-03], [8.04180511e-05, 2.89701497e-04, 6.65654642e-04], [6.64714222e-05, 1.94172831e-04, 4.08622947e-04], [5.75134301e-05, 1.40309406e-04, 2.67942773e-04], [5.13853203e-05, 1.01548040e-04, 1.64550217e-04], [4.10933487e-05, 8.66966898e-05, 1.64556856e-04]]]) - eps(depth, hour, tau_bins)float644.205e-09 6.427e-09 ... 1.608e-08
array([[[4.20484047e-09, 6.42665336e-09, 1.03495228e-08], [3.39942155e-09, 6.09951608e-09, 9.95406422e-09], [3.22205779e-09, 6.00608012e-09, 9.89785322e-09], [2.43384740e-09, 5.65188162e-09, 6.43540665e-09], [2.34056719e-09, 4.68248253e-09, 5.86884639e-09], [2.42516755e-09, 4.36594516e-09, 6.04769734e-09], [2.01203910e-09, 4.50894201e-09, 6.80387559e-09], [2.77474401e-09, 4.06819005e-09, 5.78686391e-09], [3.02695691e-09, 4.19429197e-09, 9.25259960e-09], [2.16803642e-09, 4.07562895e-09, 7.99217582e-09], [2.25024698e-09, 3.86947366e-09, 1.08315576e-08], [2.34519609e-09, 4.58983072e-09, 8.80408647e-09], [2.43251603e-09, 5.04441870e-09, 1.00646850e-08], [2.87316856e-09, 4.50655083e-09, 1.06291960e-08], [3.21955035e-09, 5.40452670e-09, 1.13614463e-08], [2.93556264e-09, 5.97819732e-09, 1.35444442e-08], [3.32274328e-09, 6.36330330e-09, 1.69035479e-08], [3.41472292e-09, 7.45018817e-09, 1.40619889e-08], [4.16563716e-09, 6.50261284e-09, 1.49772749e-08], [3.93711329e-09, 8.03877507e-09, 1.71921395e-08], ... [4.32062308e-09, 2.14336097e-08, 1.84017958e-07], [4.32244969e-09, 3.23795526e-08, 1.77984766e-07], [4.99443371e-09, 4.76849714e-08, 1.82287235e-07], [6.67475746e-09, 7.43192793e-08, 1.80947783e-07], [7.69190689e-09, 8.48603912e-08, 1.83042578e-07], [1.24068720e-08, 8.70767941e-08, 1.63145058e-07], [1.75400970e-08, 9.81732289e-08, 1.55381279e-07], [2.07108437e-08, 8.34432489e-08, 1.47163501e-07], [2.09570176e-08, 9.55806383e-08, 1.28104332e-07], [2.49357959e-08, 7.48081255e-08, 1.38691872e-07], [2.02488017e-08, 7.54215709e-08, 1.17639124e-07], [2.72920187e-08, 7.42685031e-08, 1.22579504e-07], [2.84401490e-08, 7.54687544e-08, 1.20862216e-07], [2.32893736e-08, 6.57975258e-08, 9.00575173e-08], [1.80033299e-08, 4.22211119e-08, 6.23893426e-08], [1.57912073e-08, 2.80175873e-08, 4.02353834e-08], [1.23950854e-08, 2.20880606e-08, 2.95446015e-08], [1.11499973e-08, 1.59336048e-08, 1.98472383e-08], [8.36356549e-09, 1.27149248e-08, 1.63302630e-08], [7.16264221e-09, 1.10760452e-08, 1.60790152e-08]]]) - chi(depth, hour, tau_bins)float641.67e-08 3.739e-08 ... 1.95e-08
array([[[1.67021892e-08, 3.73895360e-08, 7.93282652e-08], [1.45695729e-08, 3.21540306e-08, 7.51363908e-08], [1.66509708e-08, 3.17736964e-08, 7.22932589e-08], [1.00107661e-08, 3.13534986e-08, 4.77883670e-08], [1.33179056e-08, 2.40084728e-08, 3.57552816e-08], [9.22712244e-09, 2.50863744e-08, 4.33211842e-08], [8.08744573e-09, 2.38605289e-08, 3.82013297e-08], [1.09612925e-08, 2.27882209e-08, 3.99283958e-08], [1.34920747e-08, 1.97982703e-08, 4.72313840e-08], [9.85650711e-09, 2.04048889e-08, 5.14985775e-08], [1.08937936e-08, 2.35725432e-08, 6.87134261e-08], [1.21212706e-08, 2.51579652e-08, 7.15330504e-08], [1.32546719e-08, 3.39587720e-08, 9.47722781e-08], [1.69953873e-08, 2.60973815e-08, 7.29116184e-08], [1.67531863e-08, 3.07021553e-08, 6.79356643e-08], [1.77120188e-08, 4.09737308e-08, 7.29900722e-08], [1.93716942e-08, 4.31379369e-08, 9.26064400e-08], [2.09236218e-08, 4.34670007e-08, 9.19779817e-08], [2.48015149e-08, 3.95476993e-08, 8.28554984e-08], [2.05587984e-08, 5.27063058e-08, 8.39105570e-08], ... [6.58562963e-09, 4.00100193e-08, 1.82930124e-07], [8.27051334e-09, 5.94085776e-08, 1.62815748e-07], [1.05461194e-08, 7.08934926e-08, 1.68249950e-07], [1.42336592e-08, 9.11751999e-08, 1.81170217e-07], [1.72444375e-08, 9.91038146e-08, 1.50109762e-07], [2.48941347e-08, 1.02137827e-07, 1.58365512e-07], [3.49353545e-08, 1.15828778e-07, 1.27671006e-07], [4.11614687e-08, 9.85426121e-08, 1.36028494e-07], [3.54397051e-08, 9.90287508e-08, 1.15470101e-07], [4.15427310e-08, 8.94060867e-08, 1.12016269e-07], [3.92166332e-08, 8.64900353e-08, 9.95322983e-08], [5.45326891e-08, 8.52935771e-08, 1.10918666e-07], [4.42275054e-08, 7.57406371e-08, 9.45690680e-08], [4.10326472e-08, 7.06337841e-08, 7.52282802e-08], [3.53795830e-08, 4.58133432e-08, 5.44219219e-08], [2.37814091e-08, 3.15729107e-08, 3.78253266e-08], [1.95929775e-08, 2.64875960e-08, 3.30521637e-08], [1.66729435e-08, 2.15147840e-08, 2.22266528e-08], [1.50428825e-08, 1.53505017e-08, 1.73559861e-08], [1.08272541e-08, 1.40830926e-08, 1.94961160e-08]]]) - Jb(depth, hour, tau_bins)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
array([[[nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], ... [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan]]]) - Jq(depth, hour, tau_bins)float64-1.003 -1.842 ... -2.971 -4.428
array([[[ -1.00256446, -1.84244745, -2.80850936], [ -0.91887084, -1.62464068, -2.56282757], [ -0.88374135, -1.58679603, -2.88823456], [ -0.70443327, -1.6103817 , -1.77298658], [ -0.68665724, -1.27295597, -1.77228 ], [ -0.66664796, -1.19898684, -1.67626741], [ -0.59730391, -1.23465212, -1.70404798], [ -0.7235757 , -1.18544042, -1.59040206], [ -0.75019594, -1.04231212, -2.57469427], [ -0.57483421, -1.14463015, -2.24184293], [ -0.63752255, -1.10632234, -2.90389878], [ -0.65130446, -1.26093457, -2.35183439], [ -0.66722366, -1.3861301 , -2.72893158], [ -0.81851688, -1.24810229, -2.85460253], [ -0.72343038, -1.27913762, -2.93287239], [ -0.82057933, -1.73613097, -3.35338815], [ -0.92375014, -1.80837082, -4.44104197], [ -0.87323403, -2.01851581, -3.77664889], [ -1.19708228, -1.7267686 , -4.23530912], [ -0.98020427, -2.19262619, -4.13656709], ... [ -1.11297409, -5.84520527, -47.88722398], [ -1.20329183, -9.40240756, -45.37124552], [ -1.33200184, -12.95762863, -46.64407742], [ -1.73752661, -19.31055455, -45.85240017], [ -2.00999545, -20.92691833, -45.36418532], [ -3.38542206, -21.6217159 , -41.41348273], [ -4.660082 , -25.72382479, -37.29098972], [ -5.53276281, -20.1004968 , -36.00067158], [ -5.56787429, -22.40393839, -31.23276616], [ -5.82381354, -19.36849336, -34.99038496], [ -5.01723603, -19.13635109, -28.69033267], [ -6.73045836, -19.73596063, -28.18919034], [ -7.09852648, -18.76319064, -30.2266072 ], [ -6.12788 , -16.48818028, -21.37988398], [ -4.62785644, -10.76205284, -16.19281641], [ -4.21420556, -7.65540787, -10.55800383], [ -3.29425731, -5.96463463, -7.87434395], [ -2.86694385, -4.28650835, -5.42953264], [ -2.33320233, -3.38921404, -4.47756404], [ -1.82422195, -2.97098078, -4.42772743]]]) - S2(depth, hour, tau_bins)float320.0001345 0.0002001 ... 0.0001682
- long_name :
- $S^2$
array([[[0.00013448, 0.00020006, 0.00030025], [0.00013459, 0.00019925, 0.00030133], [0.00013651, 0.00019142, 0.0003029 ], [0.00012919, 0.00020452, 0.00029948], [0.00013896, 0.0002124 , 0.00030397], [0.00015213, 0.00019731, 0.00031407], [0.00014794, 0.00019219, 0.00030202], [0.00015217, 0.00019176, 0.00030711], [0.0001403 , 0.00019476, 0.00029934], [0.00013978, 0.00018397, 0.00030806], [0.00014281, 0.00018728, 0.00030986], [0.00014091, 0.00018794, 0.00031204], [0.00013823, 0.0001874 , 0.000305 ], [0.00013669, 0.00019039, 0.00031266], [0.00013547, 0.00019014, 0.00030768], [0.0001324 , 0.00018962, 0.00029873], [0.00013033, 0.00018335, 0.00029489], [0.00013093, 0.0001916 , 0.00030148], [0.00014072, 0.00018797, 0.00031962], [0.00014378, 0.00018233, 0.00031395], ... [0.00030681, 0.00022737, 0.00020181], [0.00031573, 0.00023622, 0.000205 ], [0.00032313, 0.0002395 , 0.00019791], [0.00032533, 0.00024731, 0.00017745], [0.0003447 , 0.00024576, 0.00016273], [0.00036876, 0.0002621 , 0.00016414], [0.0003664 , 0.000238 , 0.00016017], [0.00036569, 0.00023603, 0.00015653], [0.00035129, 0.00023055, 0.00014936], [0.00035962, 0.00021455, 0.00015314], [0.00033461, 0.00022228, 0.00015203], [0.00038251, 0.00024297, 0.00017307], [0.00040409, 0.000252 , 0.0001731 ], [0.00037549, 0.00023915, 0.00015291], [0.00036383, 0.00021962, 0.0001535 ], [0.00036171, 0.00020565, 0.0001415 ], [0.0003339 , 0.00020438, 0.00015409], [0.00035724, 0.00019812, 0.00015214], [0.0003461 , 0.00020423, 0.00015108], [0.0003532 , 0.00020651, 0.00016817]]], dtype=float32) - N2(depth, hour, tau_bins)float640.0002082 0.0002229 ... 2.895e-05
- long_name :
- $N^2$
array([[[2.08151738e-04, 2.22862104e-04, 2.14441416e-04], [2.16603991e-04, 2.24483495e-04, 2.21728203e-04], [2.15355923e-04, 2.21838325e-04, 2.18587475e-04], [2.15544742e-04, 2.22885541e-04, 2.12326524e-04], [2.16644011e-04, 2.13538050e-04, 2.23611350e-04], [2.09853593e-04, 2.20879385e-04, 2.19642997e-04], [1.98066216e-04, 2.21105441e-04, 2.18486263e-04], [2.16155790e-04, 2.18854377e-04, 2.23897831e-04], [2.21096656e-04, 2.18472571e-04, 2.21506161e-04], [2.16910676e-04, 2.20828730e-04, 2.15561007e-04], [2.20007830e-04, 2.25418650e-04, 2.14505446e-04], [2.13170636e-04, 2.18209400e-04, 2.26985607e-04], [2.08379942e-04, 2.20359529e-04, 2.28006372e-04], [2.18869956e-04, 2.12548021e-04, 2.37007497e-04], [2.22657337e-04, 2.18700017e-04, 2.19859678e-04], [2.16105074e-04, 2.18497812e-04, 2.29437082e-04], [2.15577658e-04, 2.11075774e-04, 2.20386303e-04], [2.14909841e-04, 2.19245199e-04, 2.10944560e-04], [2.18744613e-04, 2.23280408e-04, 2.02637232e-04], [2.18981413e-04, 2.22255352e-04, 2.07395227e-04], ... [6.96024719e-05, 4.77216368e-05, 3.52536021e-05], [7.21013450e-05, 4.76598289e-05, 3.29697584e-05], [7.22574894e-05, 5.01282745e-05, 3.12980790e-05], [7.68367868e-05, 4.81516730e-05, 3.00344601e-05], [7.68827915e-05, 4.78022934e-05, 2.65751683e-05], [7.66054714e-05, 4.80892891e-05, 2.51018158e-05], [7.63678736e-05, 4.39940175e-05, 2.65891577e-05], [7.64221137e-05, 4.27375694e-05, 2.65801164e-05], [7.63855405e-05, 4.13222946e-05, 2.54512380e-05], [7.23437008e-05, 3.95318950e-05, 2.40070337e-05], [7.32406023e-05, 3.87474723e-05, 2.17285776e-05], [7.84975393e-05, 3.86002397e-05, 2.22512416e-05], [7.96900786e-05, 3.91479868e-05, 2.12247197e-05], [7.60129546e-05, 3.42026320e-05, 2.17803309e-05], [7.44090646e-05, 3.69594533e-05, 2.22136917e-05], [7.49782129e-05, 3.66955626e-05, 2.24673463e-05], [7.37285223e-05, 3.66268637e-05, 2.49871805e-05], [7.33997174e-05, 3.74016674e-05, 2.55136179e-05], [7.08907159e-05, 4.04018172e-05, 2.61858948e-05], [6.88821220e-05, 4.19587334e-05, 2.89518715e-05]]]) - Rig(depth, hour, tau_bins)float641.406 1.252 ... 0.07694 0.05421
- long_name :
- $Ri^g$
array([[[1.40644738, 1.25178115, 0.74488556], [1.41762533, 1.35060308, 0.76782803], [1.47274045, 1.28796047, 0.71477707], [1.77206894, 1.23581944, 0.85990554], [1.42856916, 1.18887077, 0.86747588], [1.37627981, 1.250239 , 0.79682319], [1.35225077, 1.14314482, 0.89240256], [1.53488295, 1.13362321, 0.81762861], [1.43149522, 1.18854333, 0.82108334], [1.69124541, 1.31780546, 0.74364754], [1.63997589, 1.2650186 , 0.75719731], [1.60196071, 1.29321876, 0.80676174], [1.76211032, 1.29848346, 0.82638659], [1.89364181, 1.2401134 , 0.81697784], [1.75861073, 1.2281106 , 0.82555718], [1.79334254, 1.25421119, 0.87983679], [1.75460255, 1.23742487, 0.81284393], [1.6443649 , 1.20305246, 0.80500277], [1.90235508, 1.39715558, 0.75862198], [1.59556172, 1.27929263, 0.69249317], ... [0.11922037, 0.08834196, 0.05390135], [0.11364714, 0.0985151 , 0.04969669], [0.11955433, 0.09013164, 0.04259646], [0.13152116, 0.08148522, 0.04038412], [0.12462708, 0.07752266, 0.0412092 ], [0.1059685 , 0.08015321, 0.03483511], [0.11336615, 0.07559725, 0.03430897], [0.10954652, 0.07044978, 0.03097055], [0.10013768, 0.07146289, 0.03221148], [0.08570449, 0.07030557, 0.0349513 ], [0.09017043, 0.06546468, 0.03168679], [0.08669634, 0.0582093 , 0.03019886], [0.09715547, 0.06009826, 0.02640776], [0.07935822, 0.05776852, 0.03262731], [0.09296572, 0.05099493, 0.03820643], [0.08410832, 0.06245423, 0.04184208], [0.10067066, 0.0679023 , 0.04833413], [0.10772209, 0.06960988, 0.05154636], [0.096249 , 0.07780057, 0.05655469], [0.10424006, 0.07694161, 0.05421351]]]) - Rig_T(depth, hour, tau_bins)float641.901 1.204 0.6325 ... 0.12 0.1185
- long_name :
- $Ri^g_T$
array([[[1.90144926, 1.20402004, 0.63250394], [1.97722436, 1.16803555, 0.63929729], [1.82363029, 1.19271362, 0.63758984], [2.00858352, 1.17413288, 0.64556353], [1.764249 , 1.08857607, 0.63981687], [1.6722678 , 1.14029434, 0.63841363], [1.64416596, 1.07404686, 0.69016643], [1.80985361, 1.1286188 , 0.66627749], [1.84750455, 1.11619713, 0.65720696], [1.85832509, 1.14625708, 0.64646297], [1.82383741, 1.28825209, 0.60547409], [1.90534517, 1.22815954, 0.65631865], [1.92757436, 1.17589125, 0.6729475 ], [1.95646798, 1.15944217, 0.67811545], [2.15228629, 1.2346199 , 0.68925813], [2.17114092, 1.25440367, 0.67767255], [1.9283005 , 1.25106341, 0.65996391], [2.09583235, 1.16882993, 0.64695664], [2.00001933, 1.18052973, 0.63612982], [1.95096458, 1.24909609, 0.60900943], ... [0.14035465, 0.15645461, 0.13741426], [0.15093877, 0.1540195 , 0.12269896], [0.1517013 , 0.14701272, 0.10974087], [0.15332836, 0.13742055, 0.10176559], [0.14785674, 0.1249745 , 0.09094855], [0.14468148, 0.11747535, 0.08905629], [0.1371656 , 0.11502819, 0.08797114], [0.13831776, 0.10755145, 0.08060554], [0.1255792 , 0.1066724 , 0.07354782], [0.11734482, 0.09774099, 0.07285993], [0.12316585, 0.0914999 , 0.07129744], [0.11004655, 0.0806289 , 0.05700402], [0.12113939, 0.07903602, 0.04899825], [0.11156332, 0.08470378, 0.06233322], [0.11291802, 0.09061568, 0.07497081], [0.12169541, 0.09945144, 0.08995757], [0.12816946, 0.11179433, 0.0962672 ], [0.13384635, 0.11637368, 0.11296923], [0.12542545, 0.12154479, 0.11954271], [0.12356876, 0.12001706, 0.11848762]]]) - tau(hour, tau_bins)float640.02709 0.05733 ... 0.05745 0.09308
array([[0.0270877 , 0.05732647, 0.09203958], [0.02680315, 0.05759896, 0.09214987], [0.0266931 , 0.05764483, 0.09244683], [0.02699333, 0.05713952, 0.09263131], [0.02688743, 0.0573335 , 0.09261309], [0.02682686, 0.05738899, 0.0923755 ], [0.02736078, 0.05730989, 0.09163058], [0.02709459, 0.05719675, 0.09233963], [0.0260143 , 0.05757715, 0.09312405], [0.02643325, 0.05748537, 0.09276128], [0.02604512, 0.05695688, 0.09281864], [0.02653221, 0.05750187, 0.09254788], [0.02541264, 0.05698433, 0.09270642], [0.0258142 , 0.0573662 , 0.09281572], [0.02577848, 0.05747551, 0.09283653], [0.02582663, 0.05766199, 0.09305366], [0.0262083 , 0.05818581, 0.09442765], [0.02586172, 0.05809102, 0.0939246 ], [0.02600226, 0.05773976, 0.09318213], [0.02695185, 0.05763241, 0.09303027], [0.02669914, 0.05756156, 0.09276316], [0.02679048, 0.05723235, 0.09331579], [0.02705673, 0.05729915, 0.09280436], [0.02745168, 0.05745369, 0.09308222]])
- CREATION_DATE :
- 23:26 24-FEB-2021
- Data_Source :
- Global Tropical Moored Buoy Array Project Office/NOAA/PMEL
- File_info :
- Contact: Dai.C.McClurg@noaa.gov
- Request_for_acknowledgement :
- If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115
- _FillValue :
- 1.0000000409184788e+35
- array :
- TAO/TRITON
- missing_value :
- 1.0000000409184788e+35
- platform_code :
- 0n165e
- site_code :
- 0n165e
- wmo_platform_code :
- 52321
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 latitude float32 0.0 longitude float32 -140.0 reference_pressure int64 0 * hour (hour) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float64 9.959e-06 ... 0.0001646 eps (depth, hour, tau_bins) float64 4.205e-09 ... 1.608e-08 chi (depth, hour, tau_bins) float64 1.67e-08 ... 1.95e-08 Jb (depth, hour, tau_bins) float64 nan nan nan ... nan nan Jq (depth, hour, tau_bins) float64 -1.003 -1.842 ... -4.428 S2 (depth, hour, tau_bins) float32 0.0001345 ... 0.0001682 N2 (depth, hour, tau_bins) float64 0.0002082 ... 2.895e-05 Rig (depth, hour, tau_bins) float64 1.406 1.252 ... 0.05421 Rig_T (depth, hour, tau_bins) float64 1.901 1.204 ... 0.1185 tau (hour, tau_bins) float64 0.02709 0.05733 ... 0.09308 Attributes: CREATION_DATE: 23:26 24-FEB-2021 Data_Source: Global Tropical Moored Buoy Array Project O... File_info: Contact: Dai.C.McClurg@noaa.gov Request_for_acknowledgement: If you use these data in publications or pr... _FillValue: 1.0000000409184788e+35 array: TAO/TRITON missing_value: 1.0000000409184788e+35 platform_code: 0n165e site_code: 0n165e wmo_platform_code: 52321TAO- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 0.0006551
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_heat_diffusivity
array([[[1.00062630e-06, 1.00062675e-06, 1.00062948e-06], [1.00062630e-06, 1.00062675e-06, 1.00062914e-06], [1.00062630e-06, 1.00062664e-06, 1.00070815e-06], [1.00062630e-06, 1.00062675e-06, 1.00062823e-06], [1.00062630e-06, 1.00062664e-06, 1.00062800e-06], [1.00062630e-06, 1.00062664e-06, 1.00062800e-06], [1.00062630e-06, 1.00062664e-06, 1.00062812e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062664e-06, 1.00062869e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062664e-06, 1.00570742e-06], [1.00062630e-06, 1.00062664e-06, 1.00063801e-06], [1.00062630e-06, 1.00062664e-06, 1.00505042e-06], [1.00062630e-06, 1.00062664e-06, 1.00063016e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062653e-06, 1.00062778e-06], [1.00062630e-06, 1.00062664e-06, 1.00062721e-06], [1.00062630e-06, 1.00062653e-06, 1.00062732e-06], [1.00062630e-06, 1.00062653e-06, 1.00062744e-06], [1.00062618e-06, 1.00062664e-06, 1.00062789e-06], ... [1.31017505e-03, 1.84910290e-03, 3.04656965e-03], [1.20016653e-03, 1.93014904e-03, 2.43997667e-02], [1.26303639e-03, 2.55707884e-03, 3.35960016e-02], [1.21755805e-03, 3.45059624e-03, 4.70925234e-02], [1.20411417e-03, 6.63228473e-03, 5.48477545e-02], [1.20652630e-03, 1.19018322e-02, 6.12227395e-02], [1.33580097e-03, 1.68863572e-02, 6.64590597e-02], [1.38179970e-03, 2.08421741e-02, 7.17242733e-02], [1.61000830e-03, 2.49606222e-02, 7.40951598e-02], [1.71344716e-03, 2.64077000e-02, 7.43862391e-02], [1.82845863e-03, 2.77032442e-02, 7.27289319e-02], [1.99175603e-03, 2.91718896e-02, 7.13082328e-02], [1.64016616e-03, 1.40310498e-02, 5.11850938e-02], [2.00258894e-03, 2.12116283e-03, 1.37765761e-02], [1.67123857e-03, 1.55570242e-03, 4.48137894e-03], [1.43854087e-03, 6.79666526e-04, 3.39539582e-03], [2.04196898e-03, 1.61034847e-03, 7.48192775e-04], [2.00651051e-03, 1.96600612e-03, 5.42554015e-04], [1.92917790e-03, 1.86887372e-03, 4.04946972e-04], [1.80337648e-03, 1.76201062e-03, 6.55124430e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float322.922e-08 1.368e-07 ... 2.348e-08
- long_name :
- $SP$
- units :
- W/kg
array([[[2.92183362e-08, 1.36777288e-07, 2.27541989e-07], [2.90522504e-08, 1.39135309e-07, 2.38471500e-07], [3.01237151e-08, 1.37680928e-07, 2.37547908e-07], [3.13189972e-08, 1.36566356e-07, 2.33948427e-07], [3.05815284e-08, 1.31672735e-07, 2.26799813e-07], [2.99091028e-08, 1.30052570e-07, 2.21252151e-07], [2.86628925e-08, 1.24942943e-07, 2.08034521e-07], [2.91027824e-08, 1.22521584e-07, 2.01086578e-07], [3.04016972e-08, 1.19735688e-07, 1.95425770e-07], [3.16648112e-08, 1.19069099e-07, 1.88375097e-07], [3.19412052e-08, 1.16080244e-07, 1.91520044e-07], [3.19991642e-08, 1.15435903e-07, 1.95864132e-07], [3.21174944e-08, 1.14669440e-07, 1.85658010e-07], [3.06349754e-08, 1.09434126e-07, 1.88835344e-07], [2.94250633e-08, 1.05647260e-07, 1.79058816e-07], [2.91206543e-08, 1.01917209e-07, 1.72599258e-07], [2.79490671e-08, 1.02308121e-07, 1.71204775e-07], [2.62092996e-08, 1.02718658e-07, 1.88728990e-07], [2.34818103e-08, 1.03739211e-07, 1.98115629e-07], [2.53572559e-08, 1.08082645e-07, 2.19582347e-07], ... [1.05008013e-07, 1.26518316e-07, 2.35757994e-07], [1.06341147e-07, 3.14839610e-07, 4.57502239e-07], [1.06996765e-07, 3.11565771e-07, 3.97820173e-07], [1.10653318e-07, 4.02599085e-07, 3.34023014e-07], [1.23020456e-07, 3.97435031e-07, 2.64594632e-07], [1.38877965e-07, 3.69533637e-07, 2.31153578e-07], [1.55802979e-07, 3.41629004e-07, 1.98067397e-07], [1.71049919e-07, 2.96018698e-07, 1.74979576e-07], [1.85744952e-07, 2.51837662e-07, 1.61111473e-07], [1.85082229e-07, 2.25853171e-07, 1.47434548e-07], [1.84550530e-07, 2.08649141e-07, 1.40715855e-07], [1.84773924e-07, 1.95753216e-07, 1.38844769e-07], [1.71781878e-07, 1.28045869e-07, 9.42439726e-08], [2.01993771e-07, 7.92810866e-08, 3.86436980e-08], [1.56741407e-07, 1.01999774e-07, 2.64388085e-08], [1.30592014e-07, 6.58632686e-08, 2.03692281e-08], [1.72341856e-07, 9.93372993e-08, 1.35294016e-08], [1.64754084e-07, 1.01589876e-07, 1.64205378e-08], [1.52421507e-07, 9.34200841e-08, 1.83120470e-08], [1.39953386e-07, 8.80785507e-08, 2.34809825e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float323.153e-08 5.147e-08 ... 5.686e-09
- long_name :
- $χ$
- units :
- C^2/s
array([[[3.15311368e-08, 5.14684650e-08, 9.00696406e-08], [3.15410986e-08, 5.30324087e-08, 9.01792561e-08], [3.15075219e-08, 5.37205800e-08, 9.82160913e-08], [3.20624061e-08, 5.38295062e-08, 9.03460275e-08], [3.18296536e-08, 5.18737444e-08, 9.16030842e-08], [3.06467669e-08, 5.12408036e-08, 9.39065075e-08], [3.04877297e-08, 4.99954069e-08, 8.79619648e-08], [3.04382226e-08, 4.94467329e-08, 9.59539150e-08], [3.07606456e-08, 4.88297900e-08, 9.66301599e-08], [3.07989403e-08, 4.88755845e-08, 9.55644310e-08], [3.07602761e-08, 4.79528133e-08, 9.98426657e-08], [3.08518295e-08, 4.82226348e-08, 9.68349596e-08], [3.17893125e-08, 4.86282445e-08, 9.36632176e-08], [3.16449871e-08, 4.65035299e-08, 9.43617451e-08], [3.08822727e-08, 4.60562504e-08, 9.55800061e-08], [3.11754427e-08, 4.47915767e-08, 8.63022080e-08], [3.06896979e-08, 4.50557103e-08, 8.33114768e-08], [3.02294971e-08, 4.45752235e-08, 8.25163937e-08], [2.99401677e-08, 4.39887842e-08, 8.17240959e-08], [3.01301455e-08, 4.42664927e-08, 8.81232864e-08], ... [4.69678980e-08, 7.31207379e-08, 1.80065271e-07], [4.44437944e-08, 1.60526312e-07, 5.26232157e-07], [4.59130831e-08, 2.16911602e-07, 4.20312915e-07], [4.35025953e-08, 2.93830965e-07, 3.34603868e-07], [4.81800519e-08, 2.92471725e-07, 2.25865008e-07], [5.43300516e-08, 2.66074068e-07, 1.74853156e-07], [6.02832841e-08, 2.26358566e-07, 1.33450698e-07], [6.76159573e-08, 1.83526524e-07, 1.02248393e-07], [7.15624537e-08, 1.46292123e-07, 8.52350865e-08], [6.93415956e-08, 1.20850444e-07, 7.18475022e-08], [6.45878373e-08, 1.03103801e-07, 6.89958028e-08], [6.24963974e-08, 8.90549074e-08, 6.63747244e-08], [3.71018416e-08, 3.94645703e-08, 4.55000446e-08], [6.99339324e-08, 8.64094130e-09, 1.39391068e-08], [5.24933412e-08, 9.25000787e-09, 4.61419702e-09], [5.24419441e-08, 6.12304518e-09, 5.45677326e-09], [1.02914335e-07, 2.33580533e-08, 2.31011832e-09], [1.05326329e-07, 3.94027495e-08, 2.59428723e-09], [9.97600011e-08, 4.49156161e-08, 3.48193008e-09], [8.96089247e-08, 4.73158046e-08, 5.68561020e-09]]], dtype=float32) - Jb(depth, hour, tau_bins)float32-3.583e-10 -4.566e-10 ... -4.18e-10
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
array([[[-3.58277630e-10, -4.56598898e-10, -6.36808883e-10], [-3.61272345e-10, -4.61960303e-10, -6.18004647e-10], [-3.62497338e-10, -4.62224758e-10, -6.27580043e-10], [-3.65806746e-10, -4.59576710e-10, -6.01129591e-10], [-3.65918185e-10, -4.55617932e-10, -6.07656758e-10], [-3.62261526e-10, -4.51856774e-10, -6.12711770e-10], [-3.61771529e-10, -4.47667459e-10, -6.07582207e-10], [-3.55180552e-10, -4.47039850e-10, -6.31304398e-10], [-3.58695351e-10, -4.45621262e-10, -6.39133413e-10], [-3.58532593e-10, -4.43890119e-10, -6.39311493e-10], [-3.59802993e-10, -4.41411629e-10, -6.60119959e-10], [-3.58949426e-10, -4.43239168e-10, -6.37624564e-10], [-3.60242253e-10, -4.42435810e-10, -6.33906649e-10], [-3.61484953e-10, -4.32591463e-10, -6.34564401e-10], [-3.57910229e-10, -4.29199953e-10, -6.46178833e-10], [-3.57660401e-10, -4.23346747e-10, -6.13737727e-10], [-3.55238977e-10, -4.25607383e-10, -5.97280669e-10], [-3.52947865e-10, -4.24758007e-10, -5.87040860e-10], [-3.49784535e-10, -4.23031971e-10, -5.85980042e-10], [-3.53015478e-10, -4.23206359e-10, -6.03543548e-10], ... [-5.11393239e-09, -1.35741001e-08, -1.60814352e-07], [-5.44395062e-09, -2.43575133e-08, -1.45002247e-07], [-4.86894791e-09, -4.12963530e-08, -1.47366620e-07], [-5.39292611e-09, -5.72370809e-08, -1.25824272e-07], [-6.41620401e-09, -6.45678071e-08, -1.10190449e-07], [-6.78160905e-09, -6.84167958e-08, -9.38289020e-08], [-7.29576932e-09, -6.47739071e-08, -7.68193615e-08], [-8.18336510e-09, -5.81149209e-08, -7.04859318e-08], [-7.76166331e-09, -5.19686871e-08, -6.43213554e-08], [-7.04196568e-09, -4.70384727e-08, -6.16404350e-08], [-7.90036125e-09, -4.03301179e-08, -6.00140240e-08], [-4.88817342e-09, -1.86461691e-08, -3.97448545e-08], [-7.09699721e-09, -1.72511705e-09, -1.00553397e-08], [-4.06853129e-09, -1.28853817e-09, -2.58423283e-09], [-2.98437519e-09, -1.13034027e-09, -3.60601060e-09], [-1.09980309e-08, -2.05348050e-09, -5.57511981e-10], [-1.15927854e-08, -4.67873607e-09, -3.97649857e-10], [-1.14538530e-08, -5.87054716e-09, -3.38541611e-10], [-1.07085452e-08, -6.21498941e-09, -4.18006518e-10]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.5587 -0.7393 ... -28.37 -5.81
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[ -0.55872717, -0.73933429, -1.01263507], [ -0.55909885, -0.74781458, -1.02032381], [ -0.56339972, -0.75235563, -1.04114909], [ -0.56574466, -0.75408049, -1.00271978], [ -0.56327974, -0.74221028, -0.9980942 ], [ -0.55951453, -0.73760955, -1.01441338], [ -0.55592129, -0.73105519, -0.98191639], [ -0.55362725, -0.72750384, -1.02130405], [ -0.56301865, -0.7252834 , -1.01789328], [ -0.55946494, -0.71693061, -1.03545581], [ -0.55886687, -0.71391623, -1.07142686], [ -0.55816062, -0.71663231, -1.04043361], [ -0.56094336, -0.71781622, -1.06883406], [ -0.55902317, -0.70272396, -1.05033251], [ -0.55030415, -0.69910558, -1.06303512], [ -0.55435328, -0.68816715, -0.99056348], [ -0.54662938, -0.68960124, -0.96040917], [ -0.5458248 , -0.68627143, -0.95080166], [ -0.54284984, -0.67667759, -0.94810626], [ -0.54519989, -0.67693639, -0.979253 ], ... [ -22.76061424, -33.70290482, -94.29752641], [ -20.68569739, -42.29942 , -335.72166172], [ -21.75314978, -72.79134959, -352.68323712], [ -20.33633891, -130.71658974, -353.91620765], [ -20.80126538, -172.30126404, -320.39020802], [ -22.98944112, -190.17481245, -293.028654 ], [ -25.53994451, -196.43233475, -267.77275736], [ -28.77743047, -187.48167067, -249.95806384], [ -32.94550126, -175.95360989, -226.59814176], [ -33.82984713, -164.83311868, -216.09387751], [ -33.77147363, -156.69712408, -208.27023105], [ -35.7552922 , -146.57650858, -204.76277986], [ -25.48603855, -75.301242 , -140.61098295], [ -33.06930083, -12.40822579, -42.20722391], [ -26.257377 , -10.45363752, -15.4468764 ], [ -24.46266471, -5.90238543, -13.88000668], [ -40.2315518 , -17.05878402, -4.50023447], [ -42.74567498, -27.01418861, -3.93155364], [ -40.26200269, -28.88526491, -3.4606702 ], [ -36.62022982, -28.36562722, -5.8097894 ]]]) - S2(depth, hour, tau_bins)float320.000118 0.0002866 ... 2.19e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[1.18035634e-04, 2.86617229e-04, 3.68310226e-04], [1.19055607e-04, 2.93288234e-04, 3.68738431e-04], [1.21223944e-04, 2.90943921e-04, 3.86649830e-04], [1.24606799e-04, 2.89873598e-04, 4.03484708e-04], [1.22047291e-04, 2.80318171e-04, 4.00653458e-04], [1.19750825e-04, 2.80132226e-04, 3.87836946e-04], [1.17275791e-04, 2.74971506e-04, 3.79573670e-04], [1.19338751e-04, 2.72942707e-04, 3.62854218e-04], [1.24937898e-04, 2.71977799e-04, 3.54683434e-04], [1.28315500e-04, 2.71855562e-04, 3.48416914e-04], [1.28547457e-04, 2.69131095e-04, 3.54306278e-04], [1.29285705e-04, 2.68015225e-04, 3.56286910e-04], [1.28866639e-04, 2.66737159e-04, 3.46874818e-04], [1.24962899e-04, 2.58250860e-04, 3.61833983e-04], [1.19088931e-04, 2.55677296e-04, 3.59144819e-04], [1.16096089e-04, 2.48583849e-04, 3.58901307e-04], [1.14474693e-04, 2.44785100e-04, 3.55322234e-04], [1.08996122e-04, 2.44438765e-04, 3.55026539e-04], [1.02533719e-04, 2.44985917e-04, 3.44370579e-04], [1.07392145e-04, 2.47505552e-04, 3.61505779e-04], ... [7.28670711e-05, 6.50857328e-05, 6.72916212e-05], [7.45321959e-05, 8.67422787e-05, 4.64802906e-05], [7.49734172e-05, 9.21883038e-05, 2.75155944e-05], [7.82151910e-05, 9.42244806e-05, 1.68252045e-05], [8.51124059e-05, 7.84320728e-05, 1.13622445e-05], [8.91593227e-05, 6.00015410e-05, 8.76838749e-06], [9.46019863e-05, 4.17266747e-05, 6.79051436e-06], [9.57649972e-05, 2.93372104e-05, 5.51613903e-06], [9.58165911e-05, 2.10807702e-05, 4.71648173e-06], [9.64381688e-05, 1.72020154e-05, 4.28609746e-06], [9.73792412e-05, 1.46298644e-05, 4.15710019e-06], [9.31051254e-05, 1.27367175e-05, 4.07165271e-06], [1.01596459e-04, 1.21337544e-05, 3.59626006e-06], [1.06722146e-04, 3.36617632e-05, 3.80803567e-06], [1.02554128e-04, 4.74796179e-05, 5.74421392e-06], [9.62065242e-05, 5.39669963e-05, 6.05823743e-06], [9.68023087e-05, 5.40903638e-05, 8.59525062e-06], [9.29079615e-05, 5.20505928e-05, 1.37420657e-05], [8.81354208e-05, 4.90190614e-05, 1.74025445e-05], [8.50617798e-05, 4.72261891e-05, 2.19031808e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002707 0.0002679 ... 1.066e-05
- long_name :
- $N^2$
- units :
- s$^{-2}$
array([[[2.70653982e-04, 2.67854164e-04, 2.60556408e-04], [2.70489603e-04, 2.67931056e-04, 2.61637673e-04], [2.68508651e-04, 2.68075935e-04, 2.64729460e-04], [2.66491756e-04, 2.67974130e-04, 2.71308294e-04], [2.67325930e-04, 2.67938973e-04, 2.70344783e-04], [2.69618089e-04, 2.67519441e-04, 2.64699716e-04], [2.69445707e-04, 2.67475261e-04, 2.66230258e-04], [2.67186144e-04, 2.68850679e-04, 2.58856395e-04], [2.71684374e-04, 2.66605173e-04, 2.58436747e-04], [2.69634824e-04, 2.68864445e-04, 2.49675912e-04], [2.70803575e-04, 2.66544346e-04, 2.50487588e-04], [2.71231111e-04, 2.66110990e-04, 2.51161167e-04], [2.71857542e-04, 2.66223098e-04, 2.52874015e-04], [2.72617326e-04, 2.63849157e-04, 2.66338582e-04], [2.71545228e-04, 2.64220784e-04, 2.70796998e-04], [2.70907389e-04, 2.63898313e-04, 2.71208119e-04], [2.73490848e-04, 2.62059155e-04, 2.72291130e-04], [2.73298821e-04, 2.62513553e-04, 2.72275705e-04], [2.71756551e-04, 2.64893140e-04, 2.73101410e-04], [2.73922691e-04, 2.64201226e-04, 2.70985212e-04], ... [2.66783991e-05, 2.24435680e-05, 2.01482108e-05], [2.76282153e-05, 2.52834870e-05, 1.53130677e-05], [2.78609550e-05, 2.53739199e-05, 1.07697870e-05], [2.88161682e-05, 2.38241410e-05, 7.61099909e-06], [3.00592146e-05, 1.97585177e-05, 5.86610713e-06], [3.10059622e-05, 1.59887131e-05, 5.00141596e-06], [3.13461569e-05, 1.26825598e-05, 4.25584130e-06], [3.19165847e-05, 9.92587957e-06, 3.65537994e-06], [3.14851823e-05, 7.92554147e-06, 3.33652520e-06], [3.05563590e-05, 6.80374478e-06, 3.12953216e-06], [3.05634167e-05, 6.46581339e-06, 3.12648399e-06], [3.00894790e-05, 5.96192513e-06, 3.03067145e-06], [3.18050443e-05, 5.42599446e-06, 3.01429736e-06], [3.27169000e-05, 9.65514482e-06, 2.92601317e-06], [3.30679140e-05, 1.31469769e-05, 3.74685192e-06], [3.10162686e-05, 1.42103781e-05, 4.23103938e-06], [3.14569043e-05, 1.57434079e-05, 5.41504414e-06], [3.13854980e-05, 1.62794604e-05, 7.40211590e-06], [3.05814792e-05, 1.66734153e-05, 9.08484435e-06], [2.97653733e-05, 1.71468819e-05, 1.06635689e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float321.866 0.6854 ... 0.4224 0.5125
- long_name :
- $Ri^g$
array([[[1.8662848 , 0.68544173, 0.5350752 ], [1.8600645 , 0.6764416 , 0.52997637], [1.8082513 , 0.6684065 , 0.51994437], [1.7514868 , 0.66514564, 0.53821963], [1.7742116 , 0.6801465 , 0.5362674 ], [1.8050451 , 0.6883631 , 0.5284693 ], [1.826543 , 0.6967864 , 0.53718793], [1.839041 , 0.69765747, 0.538539 ], [1.8039743 , 0.69942325, 0.54343367], [1.762919 , 0.7034895 , 0.5462879 ], [1.7395374 , 0.71042335, 0.5473652 ], [1.743981 , 0.7068553 , 0.5498181 ], [1.7485907 , 0.7094732 , 0.5552983 ], [1.800962 , 0.7201221 , 0.56303406], [1.8770511 , 0.7300272 , 0.5768663 ], [1.8958896 , 0.7441837 , 0.59068596], [1.9319459 , 0.7465268 , 0.6032988 ], [1.9969598 , 0.76601106, 0.59619606], [2.1091611 , 0.77056324, 0.59924066], [2.072525 , 0.76344836, 0.5768527 ], ... [0.417879 , 0.3804391 , 0.3238179 ], [0.41090554, 0.30941188, 0.3488606 ], [0.403327 , 0.30385834, 0.392285 ], [0.39265534, 0.28617096, 0.46646762], [0.36620998, 0.2877478 , 0.5247332 ], [0.34303582, 0.31285378, 0.5649361 ], [0.3227075 , 0.34359443, 0.61189747], [0.3142567 , 0.38431424, 0.65183944], [0.30991292, 0.41828164, 0.6832897 ], [0.31734043, 0.44741976, 0.71992517], [0.32099736, 0.47542647, 0.747839 ], [0.33062443, 0.4994597 , 0.74357045], [0.32144618, 0.5118849 , 0.80607224], [0.31450492, 0.352032 , 0.76655614], [0.33332175, 0.34234598, 0.6389044 ], [0.35182685, 0.34710622, 0.6787852 ], [0.3533942 , 0.3665613 , 0.63400173], [0.36926734, 0.38762894, 0.5510011 ], [0.38271207, 0.4089583 , 0.52773184], [0.39180535, 0.42236108, 0.5125109 ]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float321.75 0.6778 0.516 ... 0.2708 0.353
- long_name :
- $Ri^g_T$
array([[[1.7498139 , 0.6778394 , 0.5160031 ], [1.7240694 , 0.6695879 , 0.49940598], [1.6583761 , 0.6628067 , 0.498627 ], [1.6018262 , 0.658349 , 0.51457024], [1.6139219 , 0.66992456, 0.52054894], [1.6932245 , 0.6747377 , 0.5110692 ], [1.7320414 , 0.68135345, 0.5248196 ], [1.7112534 , 0.6885524 , 0.52049387], [1.6603553 , 0.69147515, 0.5276542 ], [1.6072863 , 0.6940439 , 0.542287 ], [1.5864432 , 0.70075834, 0.5364443 ], [1.586266 , 0.70577735, 0.54150844], [1.5927796 , 0.70689046, 0.5504924 ], [1.6602877 , 0.7092931 , 0.55996585], [1.7805405 , 0.7184469 , 0.5745784 ], [1.8147937 , 0.7288473 , 0.58771074], [1.8759367 , 0.7344651 , 0.59955335], [1.9077746 , 0.7484478 , 0.59346616], [1.9881756 , 0.7515857 , 0.5964693 ], [1.9673991 , 0.7486092 , 0.56754345], ... [0.2634116 , 0.2507721 , 0.24641225], [0.2607986 , 0.21899809, 0.26634485], [0.25649 , 0.2143133 , 0.2940078 ], [0.25049907, 0.20534892, 0.32926372], [0.23761362, 0.20744732, 0.35544658], [0.22342424, 0.21846932, 0.37724414], [0.21559256, 0.2321828 , 0.39727533], [0.21003583, 0.2461529 , 0.41424218], [0.21187592, 0.26617616, 0.4138385 ], [0.21543066, 0.27246305, 0.436479 ], [0.2142856 , 0.27827302, 0.4584359 ], [0.2159077 , 0.29173055, 0.43860143], [0.20547742, 0.29568854, 0.4857257 ], [0.21092497, 0.21182545, 0.4756171 ], [0.22476283, 0.2046566 , 0.37278977], [0.23309311, 0.20733707, 0.4022013 ], [0.23358554, 0.22945035, 0.39872727], [0.24027762, 0.24394931, 0.36718082], [0.24860656, 0.26064178, 0.36847693], [0.25379637, 0.27075374, 0.35295993]]], dtype=float32) - tau(hour, tau_bins)float320.0295 0.054 ... 0.05439 0.08311
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.0295027 , 0.05400424, 0.08233272], [0.029748 , 0.0539082 , 0.08225691], [0.02973902, 0.05381709, 0.08261511], [0.02958953, 0.05378674, 0.08290136], [0.02952781, 0.0541986 , 0.08320552], [0.02957567, 0.05422445, 0.08379569], [0.02954055, 0.05467816, 0.08413152], [0.02949499, 0.05448272, 0.08375749], [0.02946378, 0.05434905, 0.08394575], [0.02955062, 0.05434605, 0.08510967], [0.02943835, 0.05417863, 0.08425807], [0.02927817, 0.05410217, 0.08441167], [0.02894278, 0.05420254, 0.08438533], [0.02929234, 0.05503666, 0.08478092], [0.0293923 , 0.05574505, 0.08499894], [0.0296241 , 0.05632416, 0.08501871], [0.03005047, 0.05656263, 0.08522117], [0.03020069, 0.05679295, 0.08566844], [0.03010707, 0.05716396, 0.08595885], [0.02992886, 0.05671743, 0.08496676], [0.03006576, 0.05620772, 0.08470601], [0.02999885, 0.05570246, 0.08516616], [0.02975408, 0.05507209, 0.08390015], [0.02965912, 0.05439087, 0.0831105 ]], dtype=float32)
- title :
- baseline
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006551 eps (depth, hour, tau_bins) float32 2.922e-08 1.368e-07 ... 2.348e-08 chi (depth, hour, tau_bins) float32 3.153e-08 5.147e-08 ... 5.686e-09 Jb (depth, hour, tau_bins) float32 -3.583e-10 ... -4.18e-10 Jq (depth, hour, tau_bins) float64 -0.5587 -0.7393 ... -28.37 -5.81 S2 (depth, hour, tau_bins) float32 0.000118 0.0002866 ... 2.19e-05 N2 (depth, hour, tau_bins) float32 0.0002707 0.0002679 ... 1.066e-05 Rig (depth, hour, tau_bins) float32 1.866 0.6854 ... 0.4224 0.5125 Rig_T (depth, hour, tau_bins) float32 1.75 0.6778 0.516 ... 0.2708 0.353 tau (hour, tau_bins) float32 0.0295 0.054 0.08233 ... 0.05439 0.08311 Attributes: title: baselinebaseline- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 0.0006196
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062664e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], ... [8.69949930e-04, 9.59244964e-04, 1.11343141e-03], [8.50179931e-04, 1.02828699e-03, 1.24498047e-02], [8.22628965e-04, 1.05729094e-03, 3.77871096e-03], [8.18862813e-04, 1.26605667e-03, 2.33795643e-02], [8.30616336e-04, 1.36252248e-03, 3.00153233e-02], [8.26583593e-04, 1.66581431e-03, 3.50915715e-02], [8.35646526e-04, 2.89651891e-03, 4.03600074e-02], [8.40844936e-04, 5.73793054e-03, 4.29606661e-02], [8.83748115e-04, 8.86736996e-03, 4.64188121e-02], [8.89282324e-04, 1.00382138e-02, 4.53674309e-02], [9.06878675e-04, 1.16374400e-02, 4.41440344e-02], [9.52538336e-04, 1.18752746e-02, 4.22920287e-02], [9.77167627e-04, 3.11270822e-03, 2.84510609e-02], [1.07384217e-03, 1.43216818e-03, 3.58461030e-03], [9.83901555e-04, 1.30531006e-03, 1.26952492e-03], [9.94352158e-04, 8.17729277e-04, 1.27175078e-03], [1.12384127e-03, 1.34078134e-03, 5.45061426e-04], [1.12999673e-03, 1.29678287e-03, 6.57544471e-04], [1.10153994e-03, 1.23138377e-03, 6.13115670e-04], [1.06722605e-03, 1.15680229e-03, 6.19593775e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float323.067e-09 6.996e-09 ... 3.577e-08
- long_name :
- $SP$
- units :
- W/kg
array([[[3.06701176e-09, 6.99570357e-09, 1.12792096e-08], [2.95429015e-09, 7.27236227e-09, 1.16628520e-08], [3.03435321e-09, 7.29928473e-09, 1.15865966e-08], [3.09861203e-09, 7.32230099e-09, 1.05787965e-08], [3.11136494e-09, 7.09674719e-09, 1.22516060e-08], [3.04559178e-09, 6.99951208e-09, 1.30705642e-08], [2.99432013e-09, 6.73037848e-09, 1.37911904e-08], [3.08337444e-09, 6.66707800e-09, 1.50147272e-08], [3.12555049e-09, 6.68693634e-09, 1.58194240e-08], [3.12062776e-09, 6.52366206e-09, 1.66755285e-08], [3.05749825e-09, 6.75726852e-09, 1.61235363e-08], [3.13664117e-09, 6.70460842e-09, 1.47471004e-08], [3.15217874e-09, 6.90299906e-09, 1.50834563e-08], [3.10377768e-09, 6.81277612e-09, 1.38752387e-08], [2.94250735e-09, 6.56541577e-09, 1.35846872e-08], [2.94795166e-09, 6.39441211e-09, 1.15830145e-08], [2.81318369e-09, 6.39819486e-09, 1.10841469e-08], [2.77422463e-09, 6.17787199e-09, 1.08094866e-08], [2.80508683e-09, 5.86984106e-09, 1.12552065e-08], [2.77357826e-09, 6.00636252e-09, 1.14595746e-08], ... [1.57990542e-07, 1.34023310e-07, 2.11007034e-07], [1.52075884e-07, 2.07358511e-07, 8.69410428e-07], [1.49262263e-07, 2.32231471e-07, 5.85107387e-07], [1.47833191e-07, 3.86520156e-07, 7.40398832e-07], [1.56316773e-07, 5.23080075e-07, 5.81518179e-07], [1.63320493e-07, 5.83236556e-07, 4.97904125e-07], [1.74322750e-07, 6.14305293e-07, 4.01600914e-07], [1.87730222e-07, 5.85696284e-07, 3.23046464e-07], [2.10503316e-07, 5.40947042e-07, 2.78580956e-07], [2.11623188e-07, 4.88402975e-07, 2.46581408e-07], [2.17197865e-07, 4.53600649e-07, 2.35122315e-07], [2.25392967e-07, 4.29075641e-07, 2.21211138e-07], [2.16730115e-07, 3.21438961e-07, 1.52973996e-07], [2.28090641e-07, 2.25466991e-07, 6.30578967e-08], [1.96342341e-07, 2.48357935e-07, 4.92314278e-08], [1.94988189e-07, 1.77086974e-07, 4.40254162e-08], [2.22069204e-07, 2.16667615e-07, 2.62699125e-08], [2.26215022e-07, 1.92706182e-07, 3.23837241e-08], [2.20546781e-07, 1.71657675e-07, 3.60358072e-08], [2.06545138e-07, 1.56897627e-07, 3.57718406e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float321.417e-08 1.12e-08 ... 1.021e-08
- long_name :
- $χ$
- units :
- C^2/s
array([[[1.41654599e-08, 1.12043317e-08, 1.14997345e-08], [1.43564414e-08, 1.10966809e-08, 1.06867253e-08], [1.40540273e-08, 1.12314247e-08, 1.03123279e-08], [1.41355176e-08, 1.13349161e-08, 1.00236583e-08], [1.42605980e-08, 1.10975300e-08, 1.07465459e-08], [1.42887382e-08, 1.11918066e-08, 1.07850138e-08], [1.43708263e-08, 1.12686127e-08, 1.08477582e-08], [1.42204337e-08, 1.12907834e-08, 1.09332676e-08], [1.43949102e-08, 1.13406422e-08, 1.08743325e-08], [1.42460177e-08, 1.14293757e-08, 1.04407292e-08], [1.40131853e-08, 1.15438645e-08, 1.04089919e-08], [1.38146916e-08, 1.15858061e-08, 1.04847828e-08], [1.37048985e-08, 1.15371819e-08, 1.06222746e-08], [1.38580338e-08, 1.15255503e-08, 1.07217994e-08], [1.39460585e-08, 1.15467724e-08, 1.07971161e-08], [1.40707135e-08, 1.14792176e-08, 1.11246070e-08], [1.44085774e-08, 1.13738761e-08, 1.11539817e-08], [1.43768464e-08, 1.14945884e-08, 1.12381482e-08], [1.44178420e-08, 1.15233316e-08, 1.11600116e-08], [1.44640957e-08, 1.12931424e-08, 1.13877565e-08], ... [1.43766911e-07, 1.03006442e-07, 8.78815598e-08], [1.37191023e-07, 1.83328794e-07, 7.09568042e-07], [1.25446263e-07, 1.67309494e-07, 3.59490400e-07], [1.29034063e-07, 2.71363888e-07, 5.55303927e-07], [1.28467946e-07, 3.13767430e-07, 4.10859371e-07], [1.31779615e-07, 3.50028699e-07, 3.47528783e-07], [1.37602086e-07, 3.64599771e-07, 2.63568069e-07], [1.47534877e-07, 3.30650295e-07, 2.13382762e-07], [1.61122401e-07, 2.85556155e-07, 1.73867647e-07], [1.52407182e-07, 2.44686731e-07, 1.56882791e-07], [1.51709003e-07, 2.20665001e-07, 1.45941712e-07], [1.50763512e-07, 1.98459645e-07, 1.46552651e-07], [1.29097174e-07, 8.24987865e-08, 7.70501956e-08], [1.57343763e-07, 4.65327865e-08, 1.47697543e-08], [1.21017209e-07, 3.88323684e-08, 6.95555213e-09], [1.44461794e-07, 2.23305978e-08, 6.47459286e-09], [1.90294784e-07, 1.18683403e-07, 3.80536269e-09], [2.08375866e-07, 1.24235868e-07, 7.61631114e-09], [2.02433256e-07, 1.21694356e-07, 1.00547233e-08], [1.91222796e-07, 1.17619521e-07, 1.02139150e-08]]], dtype=float32) - Jb(depth, hour, tau_bins)float32-2.363e-10 ... -8.176e-10
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[-2.36305003e-10, -2.03665917e-10, -2.08056850e-10], [-2.36786424e-10, -2.02221850e-10, -2.09279122e-10], [-2.33130792e-10, -2.03391748e-10, -2.06526990e-10], [-2.32916963e-10, -2.05656311e-10, -2.00404207e-10], [-2.33894487e-10, -2.05576237e-10, -2.02458952e-10], [-2.35067965e-10, -2.06578477e-10, -2.01200598e-10], [-2.36723557e-10, -2.07841605e-10, -1.97894201e-10], [-2.34675418e-10, -2.07456149e-10, -2.04126174e-10], [-2.35204634e-10, -2.06371822e-10, -2.01659162e-10], [-2.33386255e-10, -2.09510145e-10, -1.95423525e-10], [-2.32901309e-10, -2.10196471e-10, -1.95556377e-10], [-2.29793545e-10, -2.10664777e-10, -1.96119843e-10], [-2.28470382e-10, -2.09507939e-10, -1.99778888e-10], [-2.31024630e-10, -2.09797971e-10, -1.99606068e-10], [-2.35091085e-10, -2.09259596e-10, -1.99032207e-10], [-2.39884834e-10, -2.09699591e-10, -2.02228262e-10], [-2.45257092e-10, -2.07174472e-10, -2.03287082e-10], [-2.45073906e-10, -2.07818360e-10, -2.04217240e-10], [-2.46258847e-10, -2.07750095e-10, -2.05482159e-10], [-2.46289100e-10, -2.06136733e-10, -2.08115400e-10], ... [-1.06592415e-08, -1.59663145e-08, -1.32835780e-07], [-1.03445750e-08, -1.54878776e-08, -5.95220513e-08], [-1.09041141e-08, -2.38682460e-08, -1.59961715e-07], [-1.09902807e-08, -3.03844914e-08, -1.49897033e-07], [-1.09048992e-08, -3.81727965e-08, -1.61086376e-07], [-1.12891199e-08, -5.17055732e-08, -1.47693413e-07], [-1.13750245e-08, -5.63804861e-08, -1.32643848e-07], [-1.32148603e-08, -6.07146688e-08, -1.20656594e-07], [-1.23768604e-08, -5.72766012e-08, -1.13463010e-07], [-1.22800037e-08, -5.40147020e-08, -1.14951519e-07], [-1.20845041e-08, -4.98633597e-08, -1.05718762e-07], [-9.85280746e-09, -2.33101858e-08, -5.91517733e-08], [-1.16605072e-08, -4.99037567e-09, -8.04534306e-09], [-7.44766782e-09, -3.38226780e-09, -2.08077888e-09], [-8.68693206e-09, -1.48512147e-09, -3.18790239e-09], [-1.40054999e-08, -9.78285009e-09, -3.67206598e-10], [-1.44412642e-08, -1.08139648e-08, -5.36393763e-10], [-1.42424401e-08, -1.11552581e-08, -6.93334057e-10], [-1.40376359e-08, -1.11661604e-08, -8.17562462e-10]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.3639 -0.3232 ... -34.57 -7.167
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[-3.63856336e-01, -3.23210307e-01, -3.25944868e-01], [-3.64410742e-01, -3.22438626e-01, -3.14459372e-01], [-3.60407654e-01, -3.23780796e-01, -3.09333925e-01], [-3.60840765e-01, -3.25704081e-01, -3.03560250e-01], [-3.63860861e-01, -3.23031085e-01, -3.12243092e-01], [-3.65510015e-01, -3.23045562e-01, -3.13675629e-01], [-3.65869896e-01, -3.24214106e-01, -3.16109657e-01], [-3.64828718e-01, -3.25053940e-01, -3.17998895e-01], [-3.66923519e-01, -3.24808720e-01, -3.16305131e-01], [-3.64535768e-01, -3.27514952e-01, -3.11441584e-01], [-3.62548931e-01, -3.28812389e-01, -3.11511710e-01], [-3.59663507e-01, -3.29657957e-01, -3.14064940e-01], [-3.57138041e-01, -3.28465274e-01, -3.13897933e-01], [-3.58997834e-01, -3.27921523e-01, -3.16069328e-01], [-3.61749182e-01, -3.27854408e-01, -3.15479682e-01], [-3.61282345e-01, -3.26603508e-01, -3.20980527e-01], [-3.66450614e-01, -3.26124243e-01, -3.21068923e-01], [-3.64773864e-01, -3.27023533e-01, -3.22861464e-01], [-3.65693197e-01, -3.27779466e-01, -3.23091044e-01], [-3.66243552e-01, -3.25203886e-01, -3.25132215e-01], ... [-3.11515463e+01, -2.95920814e+01, -3.13240759e+01], [-2.97588069e+01, -4.01929936e+01, -3.26898750e+02], [-2.80524053e+01, -3.79606483e+01, -1.52565274e+02], [-2.87241693e+01, -5.96539726e+01, -3.31737156e+02], [-2.92523879e+01, -7.74188050e+01, -3.17598502e+02], [-2.97933349e+01, -1.03626681e+02, -3.14021997e+02], [-3.06915511e+01, -1.33709094e+02, -2.94227293e+02], [-3.16197143e+01, -1.50164779e+02, -2.75888518e+02], [-3.48487573e+01, -1.54185040e+02, -2.59916040e+02], [-3.53652229e+01, -1.47535313e+02, -2.41424957e+02], [-3.63852525e+01, -1.43487593e+02, -2.32847570e+02], [-3.87944462e+01, -1.38562537e+02, -2.20087752e+02], [-3.47095415e+01, -6.84364904e+01, -1.35048197e+02], [-3.79342051e+01, -2.57598461e+01, -2.68607238e+01], [-3.00724935e+01, -1.82040015e+01, -1.15180749e+01], [-3.35407877e+01, -9.14485579e+00, -9.79402621e+00], [-4.07352508e+01, -3.76753676e+01, -3.51846569e+00], [-4.24517449e+01, -3.83129382e+01, -5.99288931e+00], [-4.14190661e+01, -3.68610034e+01, -7.49174915e+00], [-3.99809860e+01, -3.45665587e+01, -7.16703748e+00]]]) - S2(depth, hour, tau_bins)float321.608e-05 3.475e-05 ... 4.999e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[1.60773343e-05, 3.47473433e-05, 5.45502517e-05], [1.55510897e-05, 3.62657956e-05, 5.80403103e-05], [1.57532741e-05, 3.61047132e-05, 5.88809962e-05], [1.61362404e-05, 3.65621709e-05, 5.16177024e-05], [1.63347595e-05, 3.55188567e-05, 6.21003710e-05], [1.59758565e-05, 3.49079783e-05, 6.53139577e-05], [1.57272680e-05, 3.38322498e-05, 6.75400515e-05], [1.60485488e-05, 3.36952726e-05, 7.21537217e-05], [1.64259400e-05, 3.36227749e-05, 6.97661089e-05], [1.64107878e-05, 3.27744456e-05, 8.03719886e-05], [1.60798663e-05, 3.40344159e-05, 7.83935102e-05], [1.63162404e-05, 3.36289268e-05, 7.09879023e-05], [1.63955192e-05, 3.41706000e-05, 7.23957492e-05], [1.62350207e-05, 3.38442551e-05, 6.78550277e-05], [1.52499942e-05, 3.28904680e-05, 6.58997596e-05], [1.51459171e-05, 3.21342086e-05, 5.73855250e-05], [1.46635284e-05, 3.17903687e-05, 5.46010815e-05], [1.46638777e-05, 3.11573203e-05, 5.30258621e-05], [1.46625471e-05, 2.97258357e-05, 5.51550838e-05], [1.46957009e-05, 3.03876823e-05, 5.71436394e-05], ... [1.76954345e-04, 1.31229055e-04, 1.19671509e-04], [1.75386085e-04, 1.44285470e-04, 9.24286651e-05], [1.76493719e-04, 1.65989506e-04, 9.93364883e-05], [1.77259906e-04, 1.83417767e-04, 6.67022250e-05], [1.80237097e-04, 2.02479947e-04, 4.05998107e-05], [1.86967096e-04, 2.01381699e-04, 2.87006660e-05], [1.91430430e-04, 1.85397657e-04, 2.00318373e-05], [1.97789792e-04, 1.57587536e-04, 1.56843635e-05], [2.03315052e-04, 1.19843135e-04, 1.30644439e-05], [2.03890755e-04, 9.93930225e-05, 1.25701235e-05], [2.09443897e-04, 8.31915386e-05, 1.15447201e-05], [2.06869125e-04, 7.98302353e-05, 1.13064698e-05], [2.16365268e-04, 1.05084415e-04, 9.60457783e-06], [2.22570961e-04, 1.54111607e-04, 1.23858135e-05], [2.14604661e-04, 1.80870440e-04, 1.95051653e-05], [2.11807986e-04, 1.81155774e-04, 2.01697949e-05], [2.13664738e-04, 1.67245424e-04, 2.97912757e-05], [2.12474784e-04, 1.55046437e-04, 3.86240499e-05], [2.08408193e-04, 1.45126833e-04, 4.46427548e-05], [2.01206974e-04, 1.38201241e-04, 4.99901362e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002237 0.0002014 ... 1.187e-05
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.23716459e-04, 2.01369432e-04, 2.07364807e-04], [2.24670584e-04, 2.00559298e-04, 2.04587486e-04], [2.22044764e-04, 2.02383279e-04, 2.01458635e-04], [2.21417606e-04, 2.03395641e-04, 1.95091299e-04], [2.21755530e-04, 2.02223877e-04, 2.01670875e-04], [2.21897033e-04, 2.02601805e-04, 2.01380739e-04], [2.21933631e-04, 2.02978175e-04, 2.01497838e-04], [2.20619098e-04, 2.03436794e-04, 2.02596842e-04], [2.21749637e-04, 2.02988711e-04, 2.01417221e-04], [2.19857873e-04, 2.04759723e-04, 1.99296846e-04], [2.19346490e-04, 2.05749850e-04, 1.98707756e-04], [2.18359230e-04, 2.06736469e-04, 2.01719973e-04], [2.18257366e-04, 2.06634082e-04, 1.97994610e-04], [2.19672744e-04, 2.05267948e-04, 2.00647381e-04], [2.20484260e-04, 2.05869466e-04, 1.96984562e-04], [2.22277275e-04, 2.05256205e-04, 2.00871931e-04], [2.22674425e-04, 2.04421536e-04, 2.00914845e-04], [2.22843708e-04, 2.05170858e-04, 2.03752279e-04], [2.24134274e-04, 2.05697404e-04, 2.03576696e-04], [2.23726267e-04, 2.04792828e-04, 2.06141849e-04], ... [4.44578545e-05, 3.09555980e-05, 2.18656496e-05], [4.37544404e-05, 3.26075969e-05, 2.38331431e-05], [4.39932483e-05, 3.30363655e-05, 2.08300789e-05], [4.41018747e-05, 3.41599880e-05, 1.92035659e-05], [4.40224831e-05, 3.48551039e-05, 1.46976718e-05], [4.41208649e-05, 3.49741385e-05, 1.14134200e-05], [4.50448970e-05, 3.32406053e-05, 8.23586197e-06], [4.56341149e-05, 2.86893446e-05, 7.04849163e-06], [4.63023462e-05, 2.27672244e-05, 6.17078695e-06], [4.63085671e-05, 2.06949808e-05, 5.77945048e-06], [4.70361374e-05, 1.78653881e-05, 5.42836233e-06], [4.65924677e-05, 1.72670952e-05, 5.22179744e-06], [4.66712881e-05, 1.52227567e-05, 4.89958938e-06], [4.81022325e-05, 1.52632801e-05, 4.34061121e-06], [4.82442083e-05, 2.59471744e-05, 4.41810153e-06], [4.77407229e-05, 2.84563885e-05, 5.11112830e-06], [4.76316200e-05, 2.91304204e-05, 5.96908694e-06], [4.79773953e-05, 2.88553783e-05, 8.46229523e-06], [4.80109811e-05, 2.87334278e-05, 1.05174622e-05], [4.71629573e-05, 2.89586733e-05, 1.18731123e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float3213.2 4.297 3.056 ... 0.2232 0.2512
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[13.197857 , 4.2973194 , 3.0555031 ], [13.821407 , 4.232544 , 2.5504222 ], [13.510264 , 4.2386236 , 2.4945803 ], [13.20141 , 4.2471313 , 2.4653025 ], [13.149447 , 4.319394 , 2.4220955 ], [13.85933 , 4.4229817 , 2.3923457 ], [14.107593 , 4.5487447 , 2.3519185 ], [13.647481 , 4.598955 , 2.3381877 ], [13.462015 , 4.5880175 , 2.3576286 ], [13.113719 , 4.6214905 , 2.182707 ], [13.353025 , 4.4489136 , 2.2182493 ], [13.087497 , 4.5611644 , 2.2721744 ], [12.944699 , 4.5314035 , 2.3206887 ], [12.845064 , 4.613967 , 2.453578 ], [13.476563 , 4.813492 , 2.4708343 ], [13.658272 , 5.07368 , 2.7000542 ], [14.541157 , 5.212734 , 2.7889817 ], [14.787399 , 5.2871675 , 2.8942657 ], [15.181756 , 5.5781765 , 2.8768818 ], [15.022156 , 5.3412776 , 2.904497 ], ... [ 0.2605168 , 0.24626791, 0.2186516 ], [ 0.26234898, 0.24100047, 0.27151155], [ 0.26460433, 0.22231369, 0.22628295], [ 0.26444855, 0.21137132, 0.32527298], [ 0.26379338, 0.2065137 , 0.37389553], [ 0.26258442, 0.20844041, 0.3997121 ], [ 0.25847906, 0.22196066, 0.4238111 ], [ 0.25546002, 0.2310276 , 0.44557902], [ 0.24929616, 0.24503568, 0.47095898], [ 0.25088966, 0.25116956, 0.47218096], [ 0.25009263, 0.25732547, 0.46698648], [ 0.24756661, 0.26599523, 0.46304277], [ 0.22898337, 0.22704817, 0.5012692 ], [ 0.21460304, 0.16282685, 0.3467214 ], [ 0.22194551, 0.16865185, 0.23832142], [ 0.223914 , 0.17884398, 0.23131603], [ 0.22466302, 0.19137523, 0.21537249], [ 0.22704174, 0.20382346, 0.23264292], [ 0.23163387, 0.21370158, 0.24267618], [ 0.2359288 , 0.22320737, 0.25116214]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float3213.39 4.173 3.016 ... 0.1836 0.2279
- long_name :
- $Ri^g_T$
array([[[13.39003 , 4.1733675 , 3.0161352 ], [14.119032 , 4.050316 , 2.7403107 ], [13.708002 , 4.044218 , 2.772739 ], [13.35049 , 4.09664 , 2.7337017 ], [13.440435 , 4.2385445 , 2.5692105 ], [13.888355 , 4.367624 , 2.4463243 ], [14.08926 , 4.548192 , 2.2969518 ], [13.814713 , 4.5322876 , 2.2213016 ], [13.61069 , 4.5070376 , 2.2090302 ], [13.551187 , 4.5452027 , 2.0633187 ], [13.787437 , 4.4257965 , 2.0763931 ], [13.39185 , 4.480337 , 2.1504233 ], [13.147581 , 4.426073 , 2.1903646 ], [13.178958 , 4.545905 , 2.2907367 ], [13.68602 , 4.709573 , 2.301371 ], [13.814043 , 4.880429 , 2.4320097 ], [14.562265 , 5.046787 , 2.570214 ], [14.989765 , 5.1713166 , 2.7265682 ], [15.491265 , 5.3896675 , 2.7282755 ], [15.379088 , 5.16234 , 2.7647042 ], ... [ 0.19949941, 0.19074827, 0.17702514], [ 0.20222469, 0.18197736, 0.18649122], [ 0.20144732, 0.16863236, 0.17766082], [ 0.20056407, 0.15703726, 0.18814558], [ 0.20053883, 0.14603414, 0.20615524], [ 0.1938774 , 0.14094096, 0.23102301], [ 0.18646285, 0.14410391, 0.25327593], [ 0.1793328 , 0.14658612, 0.28199226], [ 0.16956541, 0.1529384 , 0.29382858], [ 0.16857266, 0.15509175, 0.3094122 ], [ 0.16314791, 0.15785547, 0.30625468], [ 0.16122043, 0.16049628, 0.3079362 ], [ 0.15586448, 0.14820942, 0.3269163 ], [ 0.15939882, 0.13319623, 0.2494062 ], [ 0.16583118, 0.13827197, 0.19804096], [ 0.1705277 , 0.1412782 , 0.19478822], [ 0.17406169, 0.15591256, 0.21430875], [ 0.17862558, 0.16688593, 0.21968636], [ 0.1818856 , 0.17532237, 0.22169115], [ 0.18481219, 0.18359019, 0.22792467]]], dtype=float32) - tau(hour, tau_bins)float320.02931 0.05352 ... 0.0542 0.08266
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02931377, 0.05352184, 0.08240295], [0.02966809, 0.05379798, 0.08218867], [0.02963378, 0.0531844 , 0.08235388], [0.02950549, 0.05342022, 0.08332323], [0.02959662, 0.05367155, 0.08316955], [0.0294683 , 0.05404468, 0.08309559], [0.02944375, 0.05421706, 0.08337168], [0.02952339, 0.05432522, 0.08384206], [0.02939557, 0.05427076, 0.08357246], [0.02919702, 0.05400485, 0.08424108], [0.02917114, 0.05389731, 0.08418184], [0.02895542, 0.05386308, 0.08414546], [0.02882244, 0.05380915, 0.0840569 ], [0.02902017, 0.05480089, 0.08480587], [0.02929266, 0.05536883, 0.08480069], [0.02949213, 0.05573643, 0.08509558], [0.02968458, 0.05615301, 0.08533888], [0.02993085, 0.05675283, 0.08584618], [0.02995646, 0.05718726, 0.08638418], [0.02968866, 0.05656635, 0.08475825], [0.0296479 , 0.05585978, 0.08439424], [0.02981201, 0.05550362, 0.08500274], [0.02963592, 0.0546338 , 0.08361351], [0.02963188, 0.05420088, 0.08266187]], dtype=float32)
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006196 eps (depth, hour, tau_bins) float32 3.067e-09 6.996e-09 ... 3.577e-08 chi (depth, hour, tau_bins) float32 1.417e-08 1.12e-08 ... 1.021e-08 Jb (depth, hour, tau_bins) float32 -2.363e-10 ... -8.176e-10 Jq (depth, hour, tau_bins) float64 -0.3639 -0.3232 ... -34.57 -7.167 S2 (depth, hour, tau_bins) float32 1.608e-05 3.475e-05 ... 4.999e-05 N2 (depth, hour, tau_bins) float32 0.0002237 0.0002014 ... 1.187e-05 Rig (depth, hour, tau_bins) float32 13.2 4.297 3.056 ... 0.2232 0.2512 Rig_T (depth, hour, tau_bins) float32 13.39 4.173 ... 0.1836 0.2279 tau (hour, tau_bins) float32 0.02931 0.05352 0.0824 ... 0.0542 0.08266 Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5kpp.lmd.004
tree = mixpods.persist_tree(tree)
mixpods.validate_tree(tree)
Comparing shear prod to eps#
(ds.shear_prod.mean("time").hvplot.line() * ds.eps.mean("time").hvplot.line()).opts(
hv.opts.Curve(invert_axes=True, ylim=(1e-10, None), logx=True)
)
Daily composites#
S2-N2 histogram by hour of day?
daily composites by el-nino phase
figure out what wind stress is used
wind stress diurnal cycle
wind stress in model is relative wind
look at the coupled model
either turbulence parameters
Jess Masich’s papers?
MOM6 uses Large & Pond; χpods use Large & Pond
does it have gustiness; MOM6 uses wind averaged over coupling interval;
Look at 69m-89m range with medium wind stress
Need to get rid of EUC effect:
either EUC-relative coordinate system
or use K, which removes a S2 factor
Do buoyancy flux or Γ
ε, τ#
How well does the model get τ
τ bins are [0, 0.04, 0.075, inf]
def groupby_mean(node):
return node["tau"].groupby_bins(node.tau, bins=np.linspace(0, 0.2, 51)).count()
tau_hist = (
tree.dc.subset_nodes(["tau"])
.map_over_subtree(groupby_mean)
.reset_coords(drop=True)
.dc.concatenate_nodes()
)
tau_hist["tau_bins"] = pd.IntervalIndex(tau_hist.tau_bins.data).mid
tau_hist.hvplot.step(by="node")
bins = np.linspace(0, 0.2, 51)
for name, node in tree.children.items():
node["tau"].plot.hist(bins=bins, label=name, histtype="step", lw=3, density=True)
plt.legend()
dcpy.plots.linex([0.04, 0.075])
Verify depth is normalized#
for name, ds in datasets.items():
(ds.cf["sea_water_x_velocity"].cf["Z"].plot(marker=".", ls="none", label=name))
plt.legend()
<matplotlib.legend.Legend>
Compare EUC maximum and MLD#
Monthly climatology#
import tqdm
for node in tqdm.tqdm(tree.children):
tree[node]["mldT"] = tree[node]["mldT"].load()
100%|██████████| 3/3 [00:28<00:00, 9.35s/it]
def to_dataset(tree):
concat = xr.concat([v.ds for v in tree.children.values()], dim="node")
concat["node"] = list(tree.children.keys())
return concat
clim = to_dataset(
tree.map_over_subtree(
lambda x: x.reset_coords()[["mldT", "eucmax"]].groupby("time.month").mean()
)
).load()
clim
<xarray.Dataset>
Dimensions: (node: 3, month: 12)
Coordinates:
* month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
* node (node) <U16 'TAO' 'kpp.lmd.004' 'kpp.lmd.004.N150'
Data variables:
mldT (node, month) float64 -24.54 -16.38 -12.75 ... -21.87 -25.93 -31.85
eucmax (node, month) float64 -107.1 -100.9 -93.21 ... -103.6 -104.7 -103.6- node: 3
- month: 12
- month(month)int641 2 3 4 5 6 7 8 9 10 11 12
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
- node(node)<U16'TAO' ... 'kpp.lmd.004.N150'
array(['TAO', 'kpp.lmd.004', 'kpp.lmd.004.N150'], dtype='<U16')
- mldT(node, month)float64-24.54 -16.38 ... -25.93 -31.85
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
array([[-24.53757191, -16.3839409 , -12.75294118, -11.72619285, -16.07400025, -23.15900677, -25.34396312, -22.96761474, -24.52845946, -23.05868822, -27.4757473 , -27.97970513], [-31.52119396, -25.40988095, -19.22543011, -17.27927778, -22.74698925, -28.01444444, -29.92677419, -28.65774194, -26.98083333, -24.73172043, -32.70827778, -39.46795699], [-32.51075269, -23.98864087, -16.71518817, -16.10342593, -22.43136201, -26.21837963, -26.39870072, -26.2453853 , -22.56842593, -21.87235663, -25.92712963, -31.85076165]]) - eucmax(node, month)float64-107.1 -100.9 ... -104.7 -103.6
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
array([[-107.08768155, -100.866093 , -93.20755243, -80.84993961, -75.40176484, -78.4321137 , -86.90480365, -94.04602733, -101.80242583, -109.10282315, -108.64960334, -112.84834229], [-102.79130659, -97.9941869 , -92.21655108, -85.28341639, -83.98088575, -88.17257556, -92.61226102, -96.44543925, -99.76410917, -102.44588333, -105.40645361, -105.98702258], [-103.59216072, -100.00534209, -95.20661755, -88.51770763, -86.07511091, -88.81201732, -93.70615477, -97.35202638, -100.80808663, -103.62795605, -104.65148389, -103.56277501]])
- monthPandasIndex
PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
- nodePandasIndex
PandasIndex(Index(['TAO', 'kpp.lmd.004', 'kpp.lmd.004.N150'], dtype='object', name='node'))
hv.Layout(
[
hv.Overlay(
[
tree[node]
.ds["mldT"]
.reset_coords(drop=True)
.groupby("time.month")[month]
.hvplot.hist(label=node, legend=True)
.opts(frame_width=150)
for node in tree.children
]
).opts(title=str(month))
for month in np.arange(1, 13)
]
).cols(4)
(clim.mldT.hvplot(by="node") + clim.eucmax.hvplot(by="node")).cols(1)
mixpods.plot_timeseries(tree, "mldT", obs="TAO")
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/bokeh/core/property/bases.py:259: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
return new == old
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/bokeh/core/property/bases.py:259: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
return new == old
datasets["TAO"].eucmax.attrs["long_name"] = "EUC maximum"
mixpods.plot_timeseries(tree.sel(time="2008"), "eucmax", obs="TAO")
MLD Maps#
def read_sfc(casename):
path = f"/glade/scratch/dcherian/archive/{casename}/ocn/hist/*sfc*00[4-7]*.nc"
sfc = xr.open_mfdataset(
path, data_vars="minimal", coords="minimal", compat="override", parallel=True
)
return sfc
sfc = DataTree()
casenames = {
"MOM6 KPP": "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods",
"MOM6 ePBL": "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods",
}
for name, casename in casenames.items():
sfc[name] = DataTree(read_sfc(casename))
sfc
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- xh: 540
- yh: 458
- time: 10154
- nv: 2
- xq: 540
- yq: 458
- xh(xh)float64-286.7 -286.0 -285.3 ... 72.0 72.67
- long_name :
- h point nominal longitude
- units :
- degrees_east
- cartesian_axis :
- X
array([-286.666667, -286. , -285.333333, ..., 71.333333, 72. , 72.666667]) - yh(yh)float64-79.2 -79.08 -78.95 ... 87.71 87.74
- long_name :
- h point nominal latitude
- units :
- degrees_north
- cartesian_axis :
- Y
array([-79.202602, -79.076995, -78.949944, ..., 87.641507, 87.706191, 87.738663]) - time(time)object0004-01-01 12:00:00 ... 0063-12-...
- long_name :
- time
- cartesian_axis :
- T
- calendar_type :
- NOLEAP
- bounds :
- time_bnds
array([cftime.DatetimeNoLeap(4, 1, 1, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(4, 1, 2, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(4, 1, 3, 12, 0, 0, 0, has_year_zero=True), ..., cftime.DatetimeNoLeap(63, 12, 3, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(63, 12, 4, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(63, 12, 5, 12, 0, 0, 0, has_year_zero=True)], dtype=object) - nv(nv)float641.0 2.0
- long_name :
- vertex number
- units :
- none
- cartesian_axis :
- N
array([1., 2.])
- xq(xq)float64-286.3 -285.7 -285.0 ... 72.33 73.0
- long_name :
- q point nominal longitude
- units :
- degrees_east
- cartesian_axis :
- X
array([-286.333333, -285.666667, -285. , ..., 71.666667, 72.333333, 73. ]) - yq(yq)float64-79.14 -79.01 ... 87.73 87.74
- long_name :
- q point nominal latitude
- units :
- degrees_north
- cartesian_axis :
- Y
array([-79.139978, -79.013651, -78.885872, ..., 87.67781 , 87.726499, 87.7427 ])
- SSH(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Height
- units :
- m
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - tos(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Temperature
- units :
- degC
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- sea_surface_temperature
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - sos(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Salinity
- units :
- psu
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- sea_surface_salinity
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - SSU(time, yh, xq)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Zonal Velocity
- units :
- m s-1
- cell_methods :
- yh:mean xq:point time: mean
- time_avg_info :
- average_T1,average_T2,average_DT
- interp_method :
- none
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - SSV(time, yq, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Meridional Velocity
- units :
- m s-1
- cell_methods :
- yq:point xh:mean time: mean
- time_avg_info :
- average_T1,average_T2,average_DT
- interp_method :
- none
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - mass_wt(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- The column mass for calculating mass-weighted average properties
- units :
- kg m-2
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - opottempmint(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- integral_wrt_depth_of_product_of_sea_water_density_and_potential_temperature
- units :
- degC kg m-2
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- Depth integrated density times potential temperature
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - somint(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- integral_wrt_depth_of_product_of_sea_water_density_and_salinity
- units :
- psu kg m-2
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- Depth integrated density times salinity
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - Rd_dx(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Ratio between deformation radius and grid spacing
- units :
- m m-1
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - speed(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Speed
- units :
- m s-1
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - mlotst(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- units :
- m
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - oml(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- units :
- meter
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 9.36 GiB 29.25 MiB Shape (10154, 458, 540) (31, 458, 540) Dask graph 336 chunks in 673 graph layers Data type float32 numpy.ndarray - average_T1(time)objectdask.array<chunksize=(31,), meta=np.ndarray>
- long_name :
- Start time for average period
Array Chunk Bytes 79.33 kiB 248 B Shape (10154,) (31,) Dask graph 336 chunks in 673 graph layers Data type object numpy.ndarray - average_T2(time)objectdask.array<chunksize=(31,), meta=np.ndarray>
- long_name :
- End time for average period
Array Chunk Bytes 79.33 kiB 248 B Shape (10154,) (31,) Dask graph 336 chunks in 673 graph layers Data type object numpy.ndarray - average_DT(time)timedelta64[ns]dask.array<chunksize=(31,), meta=np.ndarray>
- long_name :
- Length of average period
Array Chunk Bytes 79.33 kiB 248 B Shape (10154,) (31,) Dask graph 336 chunks in 673 graph layers Data type timedelta64[ns] numpy.ndarray - time_bnds(time, nv)timedelta64[ns]dask.array<chunksize=(31, 2), meta=np.ndarray>
- long_name :
- time axis boundaries
- calendar :
- NOLEAP
Array Chunk Bytes 158.66 kiB 496 B Shape (10154, 2) (31, 2) Dask graph 336 chunks in 673 graph layers Data type timedelta64[ns] numpy.ndarray
- filename :
- gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods.mom6.sfc_0004_01.nc
- title :
- MOM6 diagnostic fields table for CESM case: gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods
- associated_files :
- areacello: gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods.mom6.static.nc
- grid_type :
- regular
- grid_tile :
- N/A
<xarray.DatasetView> Dimensions: (xh: 540, yh: 458, time: 10154, nv: 2, xq: 540, yq: 458) Coordinates: * xh (xh) float64 -286.7 -286.0 -285.3 -284.7 ... 71.33 72.0 72.67 * yh (yh) float64 -79.2 -79.08 -78.95 -78.82 ... 87.64 87.71 87.74 * time (time) object 0004-01-01 12:00:00 ... 0063-12-05 12:00:00 * nv (nv) float64 1.0 2.0 * xq (xq) float64 -286.3 -285.7 -285.0 -284.3 ... 71.67 72.33 73.0 * yq (yq) float64 -79.14 -79.01 -78.89 -78.76 ... 87.68 87.73 87.74 Data variables: (12/16) SSH (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> tos (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> sos (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> SSU (time, yh, xq) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> SSV (time, yq, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> mass_wt (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> ... ... mlotst (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> oml (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> average_T1 (time) object dask.array<chunksize=(31,), meta=np.ndarray> average_T2 (time) object dask.array<chunksize=(31,), meta=np.ndarray> average_DT (time) timedelta64[ns] dask.array<chunksize=(31,), meta=np.ndarray> time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(31, 2), meta=np.ndarray> Attributes: filename: gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixp... title: MOM6 diagnostic fields table for CESM case: gmom.e23.G... associated_files: areacello: gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseli... grid_type: regular grid_tile: N/AMOM6 KPP- xh: 540
- yh: 458
- time: 8355
- nv: 2
- xq: 540
- yq: 458
- xh(xh)float64-286.7 -286.0 -285.3 ... 72.0 72.67
- long_name :
- h point nominal longitude
- units :
- degrees_east
- cartesian_axis :
- X
array([-286.666667, -286. , -285.333333, ..., 71.333333, 72. , 72.666667]) - yh(yh)float64-79.2 -79.08 -78.95 ... 87.71 87.74
- long_name :
- h point nominal latitude
- units :
- degrees_north
- cartesian_axis :
- Y
array([-79.202602, -79.076995, -78.949944, ..., 87.641507, 87.706191, 87.738663]) - time(time)object0040-01-01 12:00:00 ... 0062-12-...
- long_name :
- time
- cartesian_axis :
- T
- calendar_type :
- NOLEAP
- bounds :
- time_bnds
array([cftime.DatetimeNoLeap(40, 1, 1, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(40, 1, 2, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(40, 1, 3, 12, 0, 0, 0, has_year_zero=True), ..., cftime.DatetimeNoLeap(62, 12, 29, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(62, 12, 30, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(62, 12, 31, 12, 0, 0, 0, has_year_zero=True)], dtype=object) - nv(nv)float641.0 2.0
- long_name :
- vertex number
- units :
- none
- cartesian_axis :
- N
array([1., 2.])
- xq(xq)float64-286.3 -285.7 -285.0 ... 72.33 73.0
- long_name :
- q point nominal longitude
- units :
- degrees_east
- cartesian_axis :
- X
array([-286.333333, -285.666667, -285. , ..., 71.666667, 72.333333, 73. ]) - yq(yq)float64-79.14 -79.01 ... 87.73 87.74
- long_name :
- q point nominal latitude
- units :
- degrees_north
- cartesian_axis :
- Y
array([-79.139978, -79.013651, -78.885872, ..., 87.67781 , 87.726499, 87.7427 ])
- SSH(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Height
- units :
- m
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 7.70 GiB 29.25 MiB Shape (8355, 458, 540) (31, 458, 540) Dask graph 276 chunks in 553 graph layers Data type float32 numpy.ndarray - tos(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Temperature
- units :
- degC
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- sea_surface_temperature
Array Chunk Bytes 7.70 GiB 29.25 MiB Shape (8355, 458, 540) (31, 458, 540) Dask graph 276 chunks in 553 graph layers Data type float32 numpy.ndarray - sos(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Salinity
- units :
- psu
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- sea_surface_salinity
Array Chunk Bytes 7.70 GiB 29.25 MiB Shape (8355, 458, 540) (31, 458, 540) Dask graph 276 chunks in 553 graph layers Data type float32 numpy.ndarray - SSU(time, yh, xq)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Zonal Velocity
- units :
- m s-1
- cell_methods :
- yh:mean xq:point time: mean
- time_avg_info :
- average_T1,average_T2,average_DT
- interp_method :
- none
Array Chunk Bytes 7.70 GiB 29.25 MiB Shape (8355, 458, 540) (31, 458, 540) Dask graph 276 chunks in 553 graph layers Data type float32 numpy.ndarray - SSV(time, yq, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Meridional Velocity
- units :
- m s-1
- cell_methods :
- yq:point xh:mean time: mean
- time_avg_info :
- average_T1,average_T2,average_DT
- interp_method :
- none
Array Chunk Bytes 7.70 GiB 29.25 MiB Shape (8355, 458, 540) (31, 458, 540) Dask graph 276 chunks in 553 graph layers Data type float32 numpy.ndarray - speed(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Sea Surface Speed
- units :
- m s-1
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 7.70 GiB 29.25 MiB Shape (8355, 458, 540) (31, 458, 540) Dask graph 276 chunks in 553 graph layers Data type float32 numpy.ndarray - mlotst(time, yh, xh)float32dask.array<chunksize=(31, 458, 540), meta=np.ndarray>
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- units :
- m
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
Array Chunk Bytes 7.70 GiB 29.25 MiB Shape (8355, 458, 540) (31, 458, 540) Dask graph 276 chunks in 553 graph layers Data type float32 numpy.ndarray - average_T1(time)objectdask.array<chunksize=(31,), meta=np.ndarray>
- long_name :
- Start time for average period
Array Chunk Bytes 65.27 kiB 248 B Shape (8355,) (31,) Dask graph 276 chunks in 553 graph layers Data type object numpy.ndarray - average_T2(time)objectdask.array<chunksize=(31,), meta=np.ndarray>
- long_name :
- End time for average period
Array Chunk Bytes 65.27 kiB 248 B Shape (8355,) (31,) Dask graph 276 chunks in 553 graph layers Data type object numpy.ndarray - average_DT(time)timedelta64[ns]dask.array<chunksize=(31,), meta=np.ndarray>
- long_name :
- Length of average period
Array Chunk Bytes 65.27 kiB 248 B Shape (8355,) (31,) Dask graph 276 chunks in 553 graph layers Data type timedelta64[ns] numpy.ndarray - time_bnds(time, nv)timedelta64[ns]dask.array<chunksize=(31, 2), meta=np.ndarray>
- long_name :
- time axis boundaries
- calendar :
- NOLEAP
Array Chunk Bytes 130.55 kiB 496 B Shape (8355, 2) (31, 2) Dask graph 276 chunks in 553 graph layers Data type timedelta64[ns] numpy.ndarray
- filename :
- gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods.mom6.sfc_0040_01.nc
- title :
- MOM6 diagnostic fields table for CESM case: gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods
- associated_files :
- areacello: gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods.mom6.static.nc
- grid_type :
- regular
- grid_tile :
- N/A
<xarray.DatasetView> Dimensions: (xh: 540, yh: 458, time: 8355, nv: 2, xq: 540, yq: 458) Coordinates: * xh (xh) float64 -286.7 -286.0 -285.3 -284.7 ... 71.33 72.0 72.67 * yh (yh) float64 -79.2 -79.08 -78.95 -78.82 ... 87.64 87.71 87.74 * time (time) object 0040-01-01 12:00:00 ... 0062-12-31 12:00:00 * nv (nv) float64 1.0 2.0 * xq (xq) float64 -286.3 -285.7 -285.0 -284.3 ... 71.67 72.33 73.0 * yq (yq) float64 -79.14 -79.01 -78.89 -78.76 ... 87.68 87.73 87.74 Data variables: SSH (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> tos (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> sos (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> SSU (time, yh, xq) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> SSV (time, yq, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> speed (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> mlotst (time, yh, xh) float32 dask.array<chunksize=(31, 458, 540), meta=np.ndarray> average_T1 (time) object dask.array<chunksize=(31,), meta=np.ndarray> average_T2 (time) object dask.array<chunksize=(31,), meta=np.ndarray> average_DT (time) timedelta64[ns] dask.array<chunksize=(31,), meta=np.ndarray> time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(31, 2), meta=np.ndarray> Attributes: filename: gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001... title: MOM6 diagnostic fields table for CESM case: gmom.e23.G... associated_files: areacello: gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseli... grid_type: regular grid_tile: N/AMOM6 ePBL
mldclim = sfc.map_over_subtree(
lambda node: node.mlotst.groupby("time.month").mean()
).compute()
filepath = (
"/glade/work/gmarques/cesm/datasets/MLD/deBoyer/deBoyer_MLD_remapped_to_tx06v1.nc"
)
mldclim["obs"] = DataTree(xr.open_dataset(filepath))
mldclim
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/coding/times.py:699: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/indexing.py:524: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
return np.asarray(array[self.key], dtype=None)
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- xh: 540
- yh: 458
- month: 12
- xh(xh)float64-286.7 -286.0 -285.3 ... 72.0 72.67
- long_name :
- h point nominal longitude
- units :
- degrees_east
- cartesian_axis :
- X
array([-286.666667, -286. , -285.333333, ..., 71.333333, 72. , 72.666667]) - yh(yh)float64-79.2 -79.08 -78.95 ... 87.71 87.74
- long_name :
- h point nominal latitude
- units :
- degrees_north
- cartesian_axis :
- Y
array([-79.202602, -79.076995, -78.949944, ..., 87.641507, 87.706191, 87.738663]) - month(month)int641 2 3 4 5 6 7 8 9 10 11 12
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
- mlotst(month, yh, xh)float32nan nan nan nan ... nan nan nan nan
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- units :
- m
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
array([[[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., ... ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
<xarray.DatasetView> Dimensions: (xh: 540, yh: 458, month: 12) Coordinates: * xh (xh) float64 -286.7 -286.0 -285.3 -284.7 ... 70.67 71.33 72.0 72.67 * yh (yh) float64 -79.2 -79.08 -78.95 -78.82 ... 87.55 87.64 87.71 87.74 * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12 Data variables: mlotst (month, yh, xh) float32 nan nan nan nan nan ... nan nan nan nan nanMOM6 KPP- xh: 540
- yh: 458
- month: 12
- xh(xh)float64-286.7 -286.0 -285.3 ... 72.0 72.67
- long_name :
- h point nominal longitude
- units :
- degrees_east
- cartesian_axis :
- X
array([-286.666667, -286. , -285.333333, ..., 71.333333, 72. , 72.666667]) - yh(yh)float64-79.2 -79.08 -78.95 ... 87.71 87.74
- long_name :
- h point nominal latitude
- units :
- degrees_north
- cartesian_axis :
- Y
array([-79.202602, -79.076995, -78.949944, ..., 87.641507, 87.706191, 87.738663]) - month(month)int641 2 3 4 5 6 7 8 9 10 11 12
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
- mlotst(month, yh, xh)float32nan nan nan nan ... nan nan nan nan
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- units :
- m
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
array([[[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., ... ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
<xarray.DatasetView> Dimensions: (xh: 540, yh: 458, month: 12) Coordinates: * xh (xh) float64 -286.7 -286.0 -285.3 -284.7 ... 70.67 71.33 72.0 72.67 * yh (yh) float64 -79.2 -79.08 -78.95 -78.82 ... 87.55 87.64 87.71 87.74 * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12 Data variables: mlotst (month, yh, xh) float32 nan nan nan nan nan ... nan nan nan nan nanMOM6 ePBL- time: 12
- yh: 458
- xh: 540
- time(time)object0001-01-15 12:00:00 ... 0001-12-...
array([cftime.DatetimeGregorian(1, 1, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 2, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 3, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 4, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 5, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 6, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 7, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 8, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 9, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 10, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 11, 15, 12, 0, 0, 0, has_year_zero=False), cftime.DatetimeGregorian(1, 12, 15, 12, 0, 0, 0, has_year_zero=False)], dtype=object) - yh(yh)float64-79.2 -79.08 -78.95 ... 87.71 87.74
array([-79.202602, -79.076995, -78.949944, ..., 87.641507, 87.706191, 87.738663]) - xh(xh)float64-286.7 -286.0 -285.3 ... 72.0 72.67
array([-286.666667, -286. , -285.333333, ..., 71.333333, 72. , 72.666667])
- mld(time, yh, xh)float64...
[2967840 values with dtype=float64]
- author :
- Gustavo Marques (gmarques@ucar.edu)
- description :
- MLD using density criterion of 0.03 kg/m3 difference (de Boyer Montegut et al., JGR 2004)
<xarray.DatasetView> Dimensions: (time: 12, yh: 458, xh: 540) Coordinates: * time (time) object 0001-01-15 12:00:00 ... 0001-12-15 12:00:00 * yh (yh) float64 -79.2 -79.08 -78.95 -78.82 ... 87.55 87.64 87.71 87.74 * xh (xh) float64 -286.7 -286.0 -285.3 -284.7 ... 70.67 71.33 72.0 72.67 Data variables: mld (time, yh, xh) float64 ... Attributes: author: Gustavo Marques (gmarques@ucar.edu) description: MLD using density criterion of 0.03 kg/m3 difference (de Bo...obs
mldclim.
mldclim["MOM6 KPP"] = DataTree(
read_sfc("gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods")
.mlotst.groupby("time.month")
.mean()
)
mldclim["MOM6 ePBL"] = DataTree(
read_sfc("gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods")
.mlotst.groupby("time.month")
.mean()
)
(
(mlotst - mldclim.mld.groupby("time.month").first())
.sel(yh=slice(-5, 5), xh=slice(-170, -80))
.plot(col="month", robust=True, col_wrap=3, aspect=2)
)
<xarray.plot.facetgrid.FacetGrid>
Mixing layer depth?#
todrop = ["TAO"]
notao = DataTree.from_dict(
{k: v.ds for k, v in tree.children.items() if k not in todrop}
)
with cfxr.set_options(custom_criteria=mixing_layer_criteria):
plot = mixpods.plot_timeseries(notao, "boundary_layer_depth", obs=None)
plot
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/cf_xarray/accessor.py:1663: UserWarning: Variables {'areacello'} not found in object but are referred to in the CF attributes.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/cf_xarray/accessor.py:1663: UserWarning: Variables {'areacello'} not found in object but are referred to in the CF attributes.
warnings.warn(
Mean profiles: mean, std#
Model mean S2 is lower by a factor of 2
S2 = mixpods.plot_profile_fill(tree, "S2", "S^2")
N2 = mixpods.plot_profile_fill(tree, "N2T", "N_T^2")
u = mixpods.plot_profile_fill(tree, "sea_water_x_velocity", "u")
T = mixpods.plot_profile_fill(tree, "sea_water_potential_temperature", "T")
Ri = mixpods.plot_median_Ri(tree)
Show code cell source Hide code cell source
S = mixpods.plot_profile_fill(tree, "sea_water_salinity", "S")
v = mixpods.plot_profile_fill(tree, "sea_water_y_velocity", "v")
# dTdz = plot_profile_fill(tree, "dTdz", "∂T/∂z")
# dSdz = plot_profile_fill(tree, "dSdz", "∂S/∂z")
((S2 + N2).opts("Curve", xrotation=30) + Ri.opts(xaxis="top", xrotation=30)).opts(
sizing_mode="stretch_width"
)
((S2 + N2).opts("Curve", xrotation=30) + Ri.opts(xaxis="top", xrotation=30)).opts(
sizing_mode="stretch_width"
)
(T + u.opts(ylim=(-0.5, 1.5))).opts(sizing_mode="stretch_width")
(T + S + u.opts(ylim=(-0.5, 1.5)) + v.opts(ylim=(-0.5, 0.5))).opts(
sizing_mode="stretch_width"
)
S2 = plot_profile_fill(tree, "S2", "S^2")
u = plot_profile_fill(tree, "sea_water_x_velocity", "u")
popts = (
hv.opts.Curve(fontscale=1.5, line_width=3, color=hv.Cycle("Dark2")),
hv.opts.Area(fill_color=hv.Cycle("Dark2")),
)
h = (S2.opts(show_legend=False) + u).opts(
hv.opts.Curve("Curve", xaxis="bottom", xlim=(-200, 0)),
hv.opts.Overlay(frame_width=400, frame_height=600),
*popts,
)
h
Remap to EUC coordinate#
gcmeq.coords["zeuc"] = gcmeq.depth - gcmeq.eucmax
remapped = flox.xarray.xarray_reduce(
gcmeq.drop_vars(["SSH", "KPPhbl", "mld", "eucmax"], errors="ignore"),
"zeuc",
dim="depth",
func="mean",
expected_groups=(np.arange(-250, 250.1, 5),),
isbin=(True,),
)
remapped["zeuc_bins"].attrs["units"] = "m"
remapped
<xarray.Dataset>
Dimensions: (time: 174000, longitude: 4, zeuc_bins: 100)
Coordinates:
latitude float64 0.025
* longitude (longitude) float64 -155.0 -140.0 -125.0 -110.0
* time (time) datetime64[ns] 1998-12-31T18:00:00 ... 2018-11-06...
cool_mask (time) bool True True True True ... False False False False
warm_mask (time) bool False False False False ... True True True True
* zeuc_bins (zeuc_bins) object (-250.0, -245.0] ... (245.0, 250.0]
Data variables: (12/25)
DFrI_TH (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
KPPdiffKzT (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
KPPg_TH (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
KPPviscAz (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
VISrI_Um (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
VISrI_Vm (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
... ...
S2 (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
shear (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
N2 (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
shred2 (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Ri (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
enso_transition (zeuc_bins, time) <U12 'La-Nina cool' ... 'El-Nino warm'
Attributes:
easting: longitude
northing: latitude
title: Station profile, index (i,j)=(1201,240)- time: 174000
- longitude: 4
- zeuc_bins: 100
- latitude()float640.025
array(0.025)
- longitude(longitude)float64-155.0 -140.0 -125.0 -110.0
array([-155.024994, -140.024994, -125.025002, -110.025002])
- time(time)datetime64[ns]1998-12-31T18:00:00 ... 2018-11-...
array(['1998-12-31T18:00:00.000000000', '1998-12-31T19:00:00.000000000', '1998-12-31T20:00:00.000000000', ..., '2018-11-06T15:00:32.000000000', '2018-11-06T16:00:16.000000000', '2018-11-06T17:00:00.000000000'], dtype='datetime64[ns]') - cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- zeuc_bins(zeuc_bins)object(-250.0, -245.0] ... (245.0, 250.0]
- axis :
- Z
- units :
- m
array([Interval(-250.0, -245.0, closed='right'), Interval(-245.0, -240.0, closed='right'), Interval(-240.0, -235.0, closed='right'), Interval(-235.0, -230.0, closed='right'), Interval(-230.0, -225.0, closed='right'), Interval(-225.0, -220.0, closed='right'), Interval(-220.0, -215.0, closed='right'), Interval(-215.0, -210.0, closed='right'), Interval(-210.0, -205.0, closed='right'), Interval(-205.0, -200.0, closed='right'), Interval(-200.0, -195.0, closed='right'), Interval(-195.0, -190.0, closed='right'), Interval(-190.0, -185.0, closed='right'), Interval(-185.0, -180.0, closed='right'), Interval(-180.0, -175.0, closed='right'), Interval(-175.0, -170.0, closed='right'), Interval(-170.0, -165.0, closed='right'), Interval(-165.0, -160.0, closed='right'), Interval(-160.0, -155.0, closed='right'), Interval(-155.0, -150.0, closed='right'), Interval(-150.0, -145.0, closed='right'), Interval(-145.0, -140.0, closed='right'), Interval(-140.0, -135.0, closed='right'), Interval(-135.0, -130.0, closed='right'), Interval(-130.0, -125.0, closed='right'), Interval(-125.0, -120.0, closed='right'), Interval(-120.0, -115.0, closed='right'), Interval(-115.0, -110.0, closed='right'), Interval(-110.0, -105.0, closed='right'), Interval(-105.0, -100.0, closed='right'), Interval(-100.0, -95.0, closed='right'), Interval(-95.0, -90.0, closed='right'), Interval(-90.0, -85.0, closed='right'), Interval(-85.0, -80.0, closed='right'), Interval(-80.0, -75.0, closed='right'), Interval(-75.0, -70.0, closed='right'), Interval(-70.0, -65.0, closed='right'), Interval(-65.0, -60.0, closed='right'), Interval(-60.0, -55.0, closed='right'), Interval(-55.0, -50.0, closed='right'), Interval(-50.0, -45.0, closed='right'), Interval(-45.0, -40.0, closed='right'), Interval(-40.0, -35.0, closed='right'), Interval(-35.0, -30.0, closed='right'), Interval(-30.0, -25.0, closed='right'), Interval(-25.0, -20.0, closed='right'), Interval(-20.0, -15.0, closed='right'), Interval(-15.0, -10.0, closed='right'), Interval(-10.0, -5.0, closed='right'), Interval(-5.0, 0.0, closed='right'), Interval(0.0, 5.0, closed='right'), Interval(5.0, 10.0, closed='right'), Interval(10.0, 15.0, closed='right'), Interval(15.0, 20.0, closed='right'), Interval(20.0, 25.0, closed='right'), Interval(25.0, 30.0, closed='right'), Interval(30.0, 35.0, closed='right'), Interval(35.0, 40.0, closed='right'), Interval(40.0, 45.0, closed='right'), Interval(45.0, 50.0, closed='right'), Interval(50.0, 55.0, closed='right'), Interval(55.0, 60.0, closed='right'), Interval(60.0, 65.0, closed='right'), Interval(65.0, 70.0, closed='right'), Interval(70.0, 75.0, closed='right'), Interval(75.0, 80.0, closed='right'), Interval(80.0, 85.0, closed='right'), Interval(85.0, 90.0, closed='right'), Interval(90.0, 95.0, closed='right'), Interval(95.0, 100.0, closed='right'), Interval(100.0, 105.0, closed='right'), Interval(105.0, 110.0, closed='right'), Interval(110.0, 115.0, closed='right'), Interval(115.0, 120.0, closed='right'), Interval(120.0, 125.0, closed='right'), Interval(125.0, 130.0, closed='right'), Interval(130.0, 135.0, closed='right'), Interval(135.0, 140.0, closed='right'), Interval(140.0, 145.0, closed='right'), Interval(145.0, 150.0, closed='right'), Interval(150.0, 155.0, closed='right'), Interval(155.0, 160.0, closed='right'), Interval(160.0, 165.0, closed='right'), Interval(165.0, 170.0, closed='right'), Interval(170.0, 175.0, closed='right'), Interval(175.0, 180.0, closed='right'), Interval(180.0, 185.0, closed='right'), Interval(185.0, 190.0, closed='right'), Interval(190.0, 195.0, closed='right'), Interval(195.0, 200.0, closed='right'), Interval(200.0, 205.0, closed='right'), Interval(205.0, 210.0, closed='right'), Interval(210.0, 215.0, closed='right'), Interval(215.0, 220.0, closed='right'), Interval(220.0, 225.0, closed='right'), Interval(225.0, 230.0, closed='right'), Interval(230.0, 235.0, closed='right'), Interval(235.0, 240.0, closed='right'), Interval(240.0, 245.0, closed='right'), Interval(245.0, 250.0, closed='right')], dtype=object)
- DFrI_TH(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - KPPdiffKzT(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - KPPg_TH(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 22301 Tasks 116 Chunks Type float32 numpy.ndarray - KPPviscAz(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - VISrI_Um(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - VISrI_Vm(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - salt(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - theta(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - u(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - v(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - w(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray - Jq_shear(time, longitude, zeuc_bins)float64dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31190 Tasks 116 Chunks Type float64 numpy.ndarray - nonlocal_flux(time, longitude, zeuc_bins)float64dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31190 Tasks 116 Chunks Type float64 numpy.ndarray - dens(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 30915 Tasks 116 Chunks Type float32 numpy.ndarray - Jq(time, longitude, zeuc_bins)float64dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 65555 Tasks 116 Chunks Type float64 numpy.ndarray - dJdz(time, longitude, zeuc_bins)float64dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 91307 Tasks 116 Chunks Type float64 numpy.ndarray - dTdt(time, longitude, zeuc_bins)float64dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
- long_name :
- $∂T/∂t = -1/(ρ_0c_p) ∂J_q/∂z$
- units :
- °C/month
Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 112767 Tasks 116 Chunks Type float64 numpy.ndarray - uz(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 10121 Tasks 116 Chunks Type float32 numpy.ndarray - vz(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 10121 Tasks 116 Chunks Type float32 numpy.ndarray - S2(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 20010 Tasks 116 Chunks Type float32 numpy.ndarray - shear(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
- long_name :
- |$u_z$|
- units :
- s$^{-1}$
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 20126 Tasks 116 Chunks Type float32 numpy.ndarray - N2(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
- long_name :
- $N^2$
- units :
- s$^{-2}$
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31727 Tasks 116 Chunks Type float32 numpy.ndarray - shred2(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
- long_name :
- Reduced shear$^2$
- units :
- $s^{-2}$
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 51389 Tasks 116 Chunks Type float32 numpy.ndarray - Ri(time, longitude, zeuc_bins)float32dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
- long_name :
- Ri
- units :
Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 51273 Tasks 116 Chunks Type float32 numpy.ndarray - enso_transition(zeuc_bins, time)<U12'La-Nina cool' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array([['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], ..., ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm']], dtype='<U12')
- easting :
- longitude
- northing :
- latitude
- title :
- Station profile, index (i,j)=(1201,240)
remapped = remapped.persist()
cluster.scale(6)
Seasonal median Ri: (0, 140)#
remapped["longitude"] = [-155.0, -140, -125, -110]
fg = (
remapped.Ri.groupby("time.season")
.median()
.sel(season=["DJF", "MAM", "JJA", "SON"], longitude=[-140, -110])
.cf.plot(
col="season", row="longitude", xlim=(0, 1.5), ylim=(-20, None), label="MITgcm"
)
)
fg.data = tao_Ri.cf.sel(quantile=0.5, longitude=[-140, -110])
fg.map_dataarray_line(
xr.plot.line, x=None, y="zeuc", hue="season", color="k", label="TAO"
)
fg.map(lambda: plt.axvline(0.25))
fg.axes[-1, -1].legend(bbox_to_anchor=(1.5, 1))
<matplotlib.legend.Legend>
1D Climatological heat budget#
Using TAO
swnet,qnetmeasurements instead of Tropflux
tree["TAO"] = DataTree(tao_gridded)
mixpods.climo_heat_budget_1d(tree["TAO"].ds, penetration="moum")
mixpods.climo_heat_budget_1d(tree["TAO"].ds, penetration="mom")
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
f, ax = plt.subplots(
1,
len(tree),
constrained_layout=True,
squeeze=False,
sharex=True,
sharey=True,
figsize=(10, 3),
)
for axx, (name, node) in zip(ax.flat, tree.children.items()):
mixpods.plot_climo_heat_budget_1d(node.ds, mxldepth=-40, penetration="mom", ax=axx)
axx.set_title(name)
dcpy.plots.clean_axes(ax)
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
mixpods.plot_climo_heat_budget_1d(tree["kpp.lmd.004.N150"].ds, penetration="mom")
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Stability diagram: 4N2-S2 plane#
Warner & Moum (2019):
Both velocity and temperature are hourly averaged before interpolation to 5-m vertical bins
Contours enclose 50% of data
Compare daily vs hourly frequency#
resampled = tree.from_dict(
{k: v.ds.resample(time="D").mean() for k, v in tree.children.items()}
)
# resampled["TAO"].update(mixpods.prepare(resampled["TAO"].ds, oni=mixpods.process_oni()))
for name, node in resampled.children.items():
node.update(
mixpods.prepare(
node.ds,
oni=tree[name]["oni"].resample(time="D").mean().reset_coords(drop=True),
)
)
node.update(mixpods.pdf_N2S2(node.ds))
mixpods.validate_tree(node)
resampled = mixpods.load_tree(resampled)
tree = mixpods.load_tree(tree)
fig = plt.figure(constrained_layout=True, figsize=(12, 6))
subfigs = fig.subfigures(2, 1)
mixpods.plot_stability_diagram_by_dataset(tree, fig=subfigs[0])
mixpods.plot_stability_diagram_by_dataset(resampled, fig=subfigs[1])
Older#
Show code cell source Hide code cell source
mixpods.plot_stability_diagram_by_dataset(datasets)
Show code cell source Hide code cell source
mixpods.plot_stability_diagram_by_phase(datasets, obs="TAO", fig=None)
Composed#
excluding MLD#
have not matched vertical grid spacing yet.
minor axis is smaller for increasing shear with KPP
prandtl number
interpolate to TAO grid
hybrid HYCOM-like case may have equatorial refinement of vertical grid above EUC
compare hybrid coordinate case resolution
instrumental error on vertical gradients for S2, N2;
if added to model, would it change the bottom tail.
Horizontal or vertical res:
MOM6 at 5km? hah
TIW compositing? La-NIna dec?
top end’s are similar, so maybe not?
sensitivity of cold tongue mean state
horizontal viscosity; controls EUC strength? (increase?)
vertical res; yan li’s paper
POP :thermocline always too diffuse and EUC too thick;
higher order interpolants can control amount diffusion when remapping
get S2, N2 from TPOSE?
Questions:
internal wave spectrum?
minor axis is smaller for model, particularly MITgcm-> too diffusive?
Can we think of this as the model relaxing to critical Ri too quickly
tree = mixpods.load_tree(tree)
fig = plt.figure(constrained_layout=True, figsize=(12, 6))
subfigs = fig.subfigures(2, 1)
mixpods.plot_stability_diagram_by_dataset(tree, fig=subfigs[0])
mixpods.plot_stability_diagram_by_phase(tree, fig=subfigs[1])
Show code cell source Hide code cell source
fig = plt.figure(constrained_layout=True, figsize=(12, 6))
subfigs = fig.subfigures(2, 1)
mixpods.plot_stability_diagram_by_dataset(tree, fig=subfigs[0])
mixpods.plot_stability_diagram_by_phase(tree, fig=subfigs[1])
including MLD#
Show code cell source Hide code cell source
fig = plt.figure(constrained_layout=True, figsize=(12, 6))
subfigs = fig.subfigures(2, 1, height_ratios=[1.2, 1])
top = subfigs[0].subfigures(1, 3, width_ratios=[1, 5, 1])
mixpods.plot_stability_diagram_by_dataset(datasets, fig=top[1])
mixpods.plot_stability_diagram_by_phase(datasets, fig=subfigs[1])
(
tao_gridded.n2s2pdf.sel(
enso_transition_phase=[
"La-Nina cool",
"La-Nina warm",
"El-Nino warm",
"El-Nino cool",
]
)
.sum("N2_bins")
.plot(hue="enso_transition_phase")
)
plt.figure()
(
tao_gridded.n2s2pdf.sel(
enso_transition_phase=[
"La-Nina cool",
"La-Nina warm",
"El-Nino warm",
"El-Nino cool",
]
)
.sum("S2_bins")
.plot(hue="enso_transition_phase")
)
[<matplotlib.lines.Line2D>,
<matplotlib.lines.Line2D>,
<matplotlib.lines.Line2D>,
<matplotlib.lines.Line2D>]
oni = pump.obs.process_oni().sel(time=slice("2005-Oct", "2015"))
(
oni.hvplot.step(color="k")
+ pump.obs.make_enso_transition_mask()
.sel(time=slice("2005-Jun", "2015"))
.reset_coords(drop=True)
.hvplot.step()
).cols(1)
Turbulence#
Dan’s suggestions#
I liked your effort to look at shear production= KmdU/dz dot dU/dz = dissipation. I mentioned that it might be worth looking at
buoyancy flux = KT dT/dz * db/dT + KS dS/dz * db/dSwith db/dT, db/dS from equation of state. I think I said KT*N^2, but I hope after checking that this is the same in this region (K_T~K_S). You could then divide buoyancy flux by 0.2 to estimate epsilon for comparison with your estimate based on shear production.
Dimensionless parameters. We alluded to this, but I think it would be interesting to explicitly quantify flux Richardson number Rif= buoyancy flux/shear production (as defined above). And Prt=KT/Km=Rif/Rig, where Rig is the usual gradient Richardson number. Following a similar logic, you might look at Reb= epsilon/nu N^2 with nu constant 1e-6 molecular viscosity. We discussed looking at diffusivity and viscosity in addition to epsilon. But note the similarity between dimensionless diffusivity KT/kappa and viscosity Km/nu, i.e. Reb=Rig*KM/nu. (with kappa a constant molecular diffusivity to make dimensionless)
I like how you continue to push what we can learn from the distributions in S2-N2 parameter space in addition to physical space. It is good for comparing with limited observations or process models. I think you can push this even further to develop metrics quantifying how the momentum and buoyancy fluxes “tango” with each other and with S2 and N2. In addition, perhaps it would be valuable to flip the perspective and look at shear-production-buoyancy flux space? Or consider various two-dimensional spaces of non-dimensional parameters mentioned above: Rig, Reb, Rif, KT/kappa, Km/nu.
Finally, I think it would be interesting to try to examine how momentum flux and shear production below the mixed layer relate to wind stress as well as the local shear. There might be a relationship that emerges in KPP, even if the K profile is formally dependent only on local Rig at these depths.
Daily composites#
h = mixpods.plot_daily_composites(
dailies.dc.subset_nodes(["S2", "N2", "Rig_T"]), logy=False
).opts(
hv.opts.Curve(frame_width=200, xrotation=45),
hv.opts.GridSpace(show_legend=True),
)
h
h = mixpods.plot_daily_composites(
dailies.dc.subset_nodes(["eps", "KT", "chi"]), logy=True
)
h.opts(
hv.opts.Curve(frame_width=200, xrotation=45),
hv.opts.GridSpace(show_legend=True),
)
ε-Ri means#
mixpods.map_hvplot(
lambda dt, name, muted: mixpods.plot_eps_ri_hist(
dt["eps_ri"].load(), label=name, muted=muted
),
tree.children,
).opts(show_legend=True).opts(hv.opts.Curve(ylim=(1e-8, 2e-6)))
Ri_f histogram#
(
np.log10(np.abs(baseline.Rif.cf.sel(Z=slice(-110, 0))))
.reset_coords(drop=True)
.hvplot.hist(bins=np.linspace(-2, 1, 100))
) * hv.VLine(np.log10(0.2)).opts(line_color="k")
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:771: RuntimeWarning: divide by zero encountered in log10
result_data = func(*input_data)
ε-Jb histogram#
from xhistogram.xarray import histogram
jpdf = histogram(
np.log10(baseline.eps),
np.log10(0.2 * baseline.eps_Jb),
density=True,
bins=np.linspace(-10, -4, 100),
).compute()
jpdf.plot.contourf(levels=52, robust=True, cmap=mpl.cm.nipy_spectral)
dcpy.plots.line45()
from mom6_tools import wright_eos
mom6tao = baseline
mom6tao["α"] = wright_eos.alpha_wright_eos(mom6tao.thetao, mom6tao.so, p=0)
mom6tao["α"].attrs = {
"standard_name": "sea_water_thermal_expansion_coefficient",
"units": "kg m-3 C-1",
}
mom6tao["β"] = wright_eos.beta_wright_eos(mom6tao.thetao, mom6tao.so, p=0)
mom6tao["β"].attrs = {
"standard_name": "sea_water_haline_contraction_coefficient",
"units": "kg m-3",
}
(
baseline.Kd_heat * baseline.α * baseline.Tz
+ baseline.Kd_heat * baseline.β * baseline.Sz
)
<xarray.DataArray (time: 507264, zi: 27, zl: 27)>
dask.array<chunksize=(8760, 26, 27), meta=np.ndarray>
Coordinates: (12/14)
* time (time) datetime64[ns] 1958-01-06T00:30:00 ... 2016-12-31...
xh float64 -140.0
yh float64 0.0625
yq float64 -0.0625
* zi (zi) float64 -230.8 -212.0 -194.4 -177.8 ... -5.0 -2.5 -0.0
eucmax (time) float64 dask.array<chunksize=(8760,), meta=np.ndarray>
... ...
en_mask (time) bool False False False False ... False False False
ln_mask (time) bool False False False False ... False False False
warm_mask (time) bool True True True True ... True True True True
cool_mask (time) bool False False False False ... False False False
enso_transition (time) <U12 '____________' ... '____________'
* zl (zl) float64 -240.8 -221.4 -203.2 ... -6.25 -3.75 -1.25
Attributes:
cell_measures: area: areacello
cell_methods: area:mean zi:point yh:mean xh:mean time: mean
long_name: Total diapycnal diffusivity for heat at interfaces
time_avg_info: average_T1,average_T2,average_DT
units: m2 s-1- time: 507264
- zi: 27
- zl: 27
- dask.array<chunksize=(8760, 26, 27), meta=np.ndarray>
Array Chunk Bytes 1.38 GiB 23.46 MiB Shape (507264, 27, 27) (8760, 26, 27) Dask graph 116 chunks in 115 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]1958-01-06T00:30:00 ... 2016-12-...
array(['1958-01-06T00:30:00.000000000', '1958-01-06T01:30:00.000000000', '1958-01-06T02:30:00.000000000', ..., '2016-12-31T21:30:00.000000000', '2016-12-31T22:30:00.000000000', '2016-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 3.87 MiB 68.44 kiB Shape (507264,) (8760,) Dask graph 58 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 3.87 MiB 68.44 kiB Shape (507264,) (8760,) Dask graph 58 chunks in 23 graph layers Data type float64 numpy.ndarray - oni(time)float32nan nan nan ... -0.3256 -0.3256
- long_name :
- ONI
- units :
- degC
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- oceanic_nino_index
array([ nan, nan, nan, ..., -0.32561368, -0.32561368, -0.32561368], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12') - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- cartesian_axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ])
- timePandasIndex
PandasIndex(DatetimeIndex(['1958-01-06 00:30:00', '1958-01-06 01:30:00', '1958-01-06 02:30:00', '1958-01-06 03:30:00', '1958-01-06 04:30:00', '1958-01-06 05:30:00', '1958-01-06 06:30:00', '1958-01-06 07:30:00', '1958-01-06 08:30:00', '1958-01-06 09:30:00', ... '2016-12-31 14:30:00', '2016-12-31 15:30:00', '2016-12-31 16:30:00', '2016-12-31 17:30:00', '2016-12-31 18:30:00', '2016-12-31 19:30:00', '2016-12-31 20:30:00', '2016-12-31 21:30:00', '2016-12-31 22:30:00', '2016-12-31 23:30:00'], dtype='datetime64[ns]', name='time', length=507264, freq=None)) - ziPandasIndex
PandasIndex(Float64Index([-230.77999999999997, -212.01999999999998, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.099999999999994, -24.809999999999995, -20.159999999999997, -16.15, -12.77, -10.0, -7.5, -5.0, -2.5, -0.0], dtype='float64', name='zi')) - zlPandasIndex
PandasIndex(Float64Index([-240.78999999999996, -221.39999999999998, -203.21499999999997, -186.13, -170.055, -154.915, -140.64499999999998, -127.19, -114.515, -102.6, -91.425, -80.97999999999999, -71.255, -62.239999999999995, -53.925, -46.3, -39.355, -33.074999999999996, -27.454999999999995, -22.484999999999996, -18.154999999999998, -14.459999999999999, -11.385, -8.75, -6.25, -3.75, -1.25], dtype='float64', name='zl'))
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Mean SST, Jq#
def flatten(tree):
varnames = next(iter(tree.children.values())).data_vars
out = xr.Dataset()
coord = xr.DataArray(list(tree.keys()), dims="node")
for var in varnames:
variables = (node[var] for name, node in tree.children.items())
out[var] = xr.concat(variables, dim=coord)
return out
sub = tree.map_over_subtree(
lambda node: (
node.cf[["Jq", "sea_surface_temperature", "ocean_vertical_heat_diffusivity"]]
.pipe(np.abs)
.cf.rename(
{"sea_surface_temperature": "sst", "ocean_vertical_heat_diffusivity": "KT"}
)
.cf.sel(Z=slice(-60, -20))
# .cf.mean("Z")
.cf.groupby("time.month")
.mean(["Z", "time"])
.load()
)
).pipe(flatten)
h = (
sub.Jq.hvplot.line(by="node", legend=True)
+ sub.sst.hvplot.line(by="node", logy=True, legend=True)
).cols(1)
h
vertical = tree.map_over_subtree(
lambda node: (
node.cf[["Jq", "ocean_vertical_heat_diffusivity"]]
.cf.rename({"ocean_vertical_heat_diffusivity": "KT"})
.cf.sel(Z=slice(-120, -20))
.groupby("time.season")
.mean()
.sel(season=["DJF", "MAM", "JJA", "SON"])
.load()
)
)
seasonal = {}
seasonal["Jq"] = mixpods.map_hvplot(
lambda ds, name, muted: (
ds["Jq"]
.cf.rename({"Z": "depth"})
.hvplot.line(y="depth", col="season", label=name, muted=muted)
),
vertical.children,
)
seasonal["KT"] = mixpods.map_hvplot(
lambda ds, name, muted: (
ds["KT"]
.cf.rename({"Z": "depth"})
.hvplot.line(logx=True, y="depth", col="season", label=name, muted=muted)
),
vertical.children,
)
def hvplot_facet(tree, varname, col, **kwargs):
by = next(iter(tree.children.values()))[col].data
handles = []
for by_ in by:
handles.append(
hv.Overlay(
[
node.ds.cf[varname]
.sel({col: by_})
.hvplot.line(label=name, **kwargs)
for name, node in tree.children.items()
]
)
)
return hv.Layout(handles)
proc = vertical.map_over_subtree(lambda node: node.cf.rename({"Z": "Z"}))
layout = hv.Layout(
[
hvplot_facet(
proc,
varname="Jq",
col="season",
),
hvplot_facet(
proc,
varname="ocean_vertical_heat_diffusivity",
col="season",
logx=True,
ylim=(1e-6, 1e-1),
),
]
)
layout.opts(
hv.opts.Curve(invert_axes=True, xrotation=20, frame_width=160, frame_height=300),
hv.opts.Overlay(legend_position="bottom_left", shared_axes=True, labelled=["x"]),
)
mixpods.map_hvplot(
lambda ds, name, muted: (ds["Tz"] < 0)
.sum("time")
.hvplot.line(label=name, muted=muted, invert=True),
tree,
)
np.log10(tree["kpp.lmd.004"]["Kd_heat"].resample(time="M").mean()).hvplot.quadmesh(
y="zi"
)
mixpods.map_hvplot(
lambda ds, name, muted: ds.ds.cf["ocean_vertical_heat_diffusivity"]
.mean("time")
.hvplot.line(label=name, muted=muted),
tree.children,
).collate().opts(
ylim=(1e-8, 1e1),
legend_position="right",
logx=True,
invert_axes=True,
frame_width=300,
aspect=1 / 3,
)
WARNING:param.OverlayPlot02012: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot02012: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.
WARNING:param.OverlayPlot02072: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot02072: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.
mixpods.map_hvplot(
lambda ds, name, muted: ds["Jq"].mean("time").hvplot.line(label=name, muted=muted),
tree.children,
).collate().opts(
legend_position="right", invert_axes=True, frame_width=300, aspect=1 / 3
)
Histograms: Ri, ε#
take out MLD?
handles = [
mixpods.plot_distributions(tree, "chi", bins=np.linspace(-11, -4, 101), log=True),
mixpods.plot_distributions(tree, "eps", bins=np.linspace(-11, -4, 101), log=True),
mixpods.plot_distributions(
tree, "ocean_vertical_heat_diffusivity", bins=np.linspace(-8, -1, 101), log=True
),
# plot_distributions(tree, "Jq", bins=np.linspace(-1000, 0, 51), log=False),
mixpods.plot_distributions(tree, "Rig_T", np.linspace(-0.5, 1.5, 61))
* hv.VLine(0.25).opts(line_color="k"),
]
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:760: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:760: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
hv.Layout(handles).opts("Overlay", frame_width=600).cols(2)
for node in ["baseline", "kpp.lmd.002", "kpp.lmd.003", "kpp.lmd.004"]:
plt.figure()
tree[node].sel(time=slice("2008-10-25", "2008-11-15")).ds.cf["ocean_vertical_heat_diffusivity"]
.cf.sel(Z=slice(-120, None)).reset_coords(drop=True).cf.plot(
cmap=mpl.cm.Spectral_r, size=3, aspect=5, norm=mpl.colors.LogNorm(1e-7, 1e-2)
)
<Figure size 896x672 with 0 Axes>
<Figure size 896x672 with 0 Axes>
<Figure size 896x672 with 0 Axes>
<Figure size 896x672 with 0 Axes>
for node in ["baseline", "kpp.lmd.002", "kpp.lmd.003", "kpp.lmd.004"]:
plt.figure()
tree[node].sel(time=slice("2008-10-25", "2008-11-15"))["eps"].cf.sel(
Z=slice(-120, None)
).reset_coords(drop=True).cf.plot(
cmap=mpl.cm.Spectral_r, size=3, aspect=5, norm=mpl.colors.LogNorm(1e-9, 1e-5)
)
<Figure size 896x672 with 0 Axes>
<Figure size 896x672 with 0 Axes>
<Figure size 896x672 with 0 Axes>
<Figure size 896x672 with 0 Axes>
Compare boundary layer depth#
hbl = (
tree.dc.extract(["baseline", "kpp.lmd.004"])
.dc.subset_nodes("KPP_OBLdepth")
.dc.concatenate_nodes()
.reset_coords(drop=True)
)
(
hbl.groupby("time.hour").mean().hvplot.line(by="node", flip_yaxis=True)
+ hbl.groupby("time.hour").median().hvplot.line(by="node", flip_yaxis=True)
+ hbl.hvplot.hist(by="node", bins=np.arange(0, 50, 1), normed=1)
).cols(1)
Panel debug#
import panel as pn
print(layout)
:Layout
.Overlay.I :Overlay
.Curve.TAO :Curve [chi_bin] (histogram_chi)
.Curve.Baseline :Curve [chi_bin] (histogram_chi)
.Curve.Epbl :Curve [chi_bin] (histogram_chi)
.Curve.Kpp_full_stop_lmd_full_stop_002 :Curve [chi_bin] (histogram_chi)
.Curve.Kpp_full_stop_lmd_full_stop_003 :Curve [chi_bin] (histogram_chi)
.Curve.Kpp_full_stop_lmd_full_stop_004 :Curve [chi_bin] (histogram_chi)
.Overlay.II :Overlay
.Curve.TAO :Curve [eps_bin] (histogram_eps)
.Curve.Baseline :Curve [eps_bin] (histogram_eps)
.Curve.Epbl :Curve [eps_bin] (histogram_eps)
.Curve.Kpp_full_stop_lmd_full_stop_002 :Curve [eps_bin] (histogram_eps)
.Curve.Kpp_full_stop_lmd_full_stop_003 :Curve [eps_bin] (histogram_eps)
.Curve.Kpp_full_stop_lmd_full_stop_004 :Curve [eps_bin] (histogram_eps)
def change_muted(value):
present = layout.Overlay.I.Curve.children
normalized = [n.replace(".", "_full_stop_") for n in value]
[n for n in normalized if n in present]
pass
def change_muted_test(value):
present = layout.Overlay.I.Curve.children
normalized = [n.replace(".", "_full_stop_") for n in value]
[n for n in normalized if n in present]
pass
names = list(tree.children.keys())
checkbox = pn.widgets.CheckBoxGroup(value=names, options=names, inline=True)
checkbox.jslink(layout, {"value": change_muted})
debug = pn.widgets.StaticText(name="debug", value="default")
checkbox.jslink(debug, dict(value=change_muted_test))
pn.Column(debug, checkbox, layout.cols(1))
bins = np.linspace(-0.5, 1.5, 61)
handles = [
hvplot_step_hist(
ds["Rig_T"].reset_coords(drop=True).cf.sel(Z=slice(-69, -29)),
name=name,
bins=bins,
)
for name, ds in datasets.items()
]
(hv.Overlay(handles) * hv.VLine(0.25).opts(line_color="k")).opts(ylabel="PDF")
ε vs Ri#
Possible reasons for difference
My MLD seems deeper even though I use the same ΔT=0.15 threshold. It could be that they’ve used Akima splines to interpolate in the vertical
~I’ve filtered to DCL, so accounting for MLD and EUC movement. I’m not sure they did that.~
Only 𝜒pods between 29 and 69 m are used in this analysis as deeper 𝜒pods are more strongly influenced by the variability of zEUC than by surface forcing.
TODO
EUC strength is proportional to horizontal visc for 1° models
Add \(ε_χ\) for MOM6
Do for K
composite by TIW strength
start with 10min χpod data, then average to hourly.
make composites off the equator: look at strong off-equatorial du/dx; du/dz
f, ax = plt.subplots(
2, len(datasets) // 2, sharex=True, sharey=True, constrained_layout=True
)
for axx, (name, ds) in zip(ax.flat, tree.children.items()):
da = np.log10(ds["eps_ri"])
# da["Rig_T_bins"] = pd.Index(
# [pd.Interval(np.log10(i.left), np.log10(i.right))
# for i in tao_gridded.Rig_T_bins.data]
# )
(
da.sel(stat="mean")
.sel(enso_transition_phase=["La-Nina cool", "El-Nino warm"])
.plot(hue="enso_transition_phase", marker=".", ax=axx, add_legend=False)
)
axx.set_title(name)
ticks = [0.04, 0.1, 0.25, 0.63, 1.6]
# axx.set_yticks([-7.25, -7, -6.5, -6])
axx.set_ylim([-7.5, -5.5])
axx.set_xticks(np.log10(ticks))
axx.set_xticklabels([f"{a:.2f}" for a in ticks])
# axx.tick_params(axis='x', rotation=30)
ax[0, 0].set_ylabel("ε")
dcpy.plots.linex(np.log10(0.25), ax=ax.flat)
# dcpy.plots.clean_axes(ax)
f.set_size_inches((8, 4))
Shear spectra#
Notes#
Now obvious difference between clockwise and counter-clockwise so I’ve chosen to just do the spectrum of the magnitude
think about tides
what is the “DATM coupling interval”
Kelvin’s KPP parameters
what are the momentum and heat fluxes over some resolvable timescale. And how do we compare to obs.
Frequency spectra @ Z= 50m#
DEPTHS = [-50, -60, -75]
f, ax = plt.subplots(
len(DEPTHS), 1, sharex="col", sharey=True, squeeze=False, constrained_layout=True
)
for name in tree.children.keys():
if name == "tropicheat":
continue
ds = tree[name].ds.reset_coords()
u = ds.cf["sea_water_x_velocity"]
v = ds.cf["sea_water_y_velocity"]
S = (u + 1j * v).cf.sel(Z=DEPTHS, method="nearest").load()
for axx, z in zip(ax, DEPTHS):
dcpy.ts.PlotSpectrum(
np.abs(S).cf.sel(Z=z, method="nearest"),
multitaper=False,
nsmooth=12,
label=name,
lw=0.75,
ax=axx[0],
)
axx[-1].text(x=0.9, y=0.9, s=f"{z} m", transform=axx[-1].transAxes)
ax.ravel()[0].set_ylim([1e-9, None])
ax.ravel()[-1].legend()
dcpy.plots.clean_axes(ax)
f.set_size_inches((4, 6))
f, ax = plt.subplots(
len(DEPTHS), 1, sharex="col", sharey=True, squeeze=False, constrained_layout=True
)
for name in tree.children.keys():
ds = tree[name].ds.reset_coords()
u = ds.cf["sea_water_x_velocity"]
v = ds.cf["sea_water_y_velocity"]
S = (
(u.cf.differentiate("Z") + 1j * v.cf.differentiate("Z"))
.cf.sel(Z=DEPTHS, method="nearest")
.load()
)
for axx, z in zip(ax, DEPTHS):
dcpy.ts.PlotSpectrum(
np.abs(S).cf.sel(Z=z, method="nearest"),
multitaper=False,
nsmooth=12,
label=name,
lw=0.75,
ax=axx[0],
)
axx[-1].text(x=0.9, y=0.9, s=f"{z} m", transform=axx[-1].transAxes)
ax.ravel()[0].set_ylim([1e-9, None])
ax.ravel()[-1].legend()
dcpy.plots.clean_axes(ax)
f.set_size_inches((4, 6))
2D frequency-wavenumber spectra#
S = (u.cf.differentiate("Z") + 1j * v.cf.differentiate("Z")).sel(time="2010")
uniform = (
S.cf.sel(Z=slice(-150, 0))
.cf.interp(Z=np.arange(-150, -52, 5))
.interpolate_na("time")
)
uniform
<xarray.DataArray (time: 8760, depth: 20)> (-0.008227312937378883-0.014154188334941864j) ... (-0.030751610174775124+0.00... Coordinates: * time (time) datetime64[ns] 2010-01-01 ... 2010-12-31T23:00:00 * depth (depth) int64 -150 -145 -140 -135 -130 -125 ... -75 -70 -65 -60 -55
- time: 8760
- depth: 20
- (-0.008227312937378883-0.014154188334941864j) ... (-0.0307516101747...
array([[-0.00822731-1.41541883e-02j, -0.01104225-1.87902600e-02j, -0.00737722-1.55558735e-02j, ..., -0.00480708-5.68800839e-04j, -0.0042146 -1.82909513e-03j, -0.0029062 +2.42253300e-04j], [-0.00531847-1.03310198e-02j, -0.01084651-2.03043409e-02j, -0.00868449-2.21009050e-02j, ..., -0.00534157-2.47339543e-04j, -0.00342131+7.36702350e-05j, 0.00010791-3.41641557e-04j], [-0.00605565-1.28854662e-02j, -0.01443065-2.17604656e-02j, -0.01254376-2.01687478e-02j, ..., -0.00577032+4.53331973e-04j, -0.00463074-4.70184867e-04j, -0.00217218+2.98153609e-05j], ..., [ 0.00611982+2.12412328e-04j, 0.00489964-4.32382477e-03j, 0.00696214-6.69882447e-03j, ..., -0.03391381+1.40654389e-02j, -0.02860131+8.12793989e-03j, -0.02517292+8.92511103e-04j], [ 0.00611557-1.86339987e-03j, 0.00853345-4.76134929e-03j, 0.00904288-4.14794963e-03j, ..., -0.03222217+1.69887263e-02j, -0.02565823+9.37682670e-03j, -0.02081093+9.08097252e-04j], [ 0.00670861-2.37500062e-03j, 0.01002112-2.37499992e-03j, 0.01039479-2.22451240e-03j, ..., -0.03115585+1.84854008e-02j, -0.03204853+1.60092376e-02j, -0.03075161+8.64249934e-03j]]) - time(time)datetime64[ns]2010-01-01 ... 2010-12-31T23:00:00
array(['2010-01-01T00:00:00.000000000', '2010-01-01T01:00:00.000000000', '2010-01-01T02:00:00.000000000', ..., '2010-12-31T21:00:00.000000000', '2010-12-31T22:00:00.000000000', '2010-12-31T23:00:00.000000000'], dtype='datetime64[ns]') - depth(depth)int64-150 -145 -140 -135 ... -65 -60 -55
- axis :
- Z
- positive :
- up
- units :
- m
array([-150, -145, -140, -135, -130, -125, -120, -115, -110, -105, -100, -95, -90, -85, -80, -75, -70, -65, -60, -55])
- timePandasIndex
PandasIndex(DatetimeIndex(['2010-01-01 00:00:00', '2010-01-01 01:00:00', '2010-01-01 02:00:00', '2010-01-01 03:00:00', '2010-01-01 04:00:00', '2010-01-01 05:00:00', '2010-01-01 06:00:00', '2010-01-01 07:00:00', '2010-01-01 08:00:00', '2010-01-01 09:00:00', ... '2010-12-31 14:00:00', '2010-12-31 15:00:00', '2010-12-31 16:00:00', '2010-12-31 17:00:00', '2010-12-31 18:00:00', '2010-12-31 19:00:00', '2010-12-31 20:00:00', '2010-12-31 21:00:00', '2010-12-31 22:00:00', '2010-12-31 23:00:00'], dtype='datetime64[ns]', name='time', length=8760, freq=None)) - depthPandasIndex
PandasIndex(Int64Index([-150, -145, -140, -135, -130, -125, -120, -115, -110, -105, -100, -95, -90, -85, -80, -75, -70, -65, -60, -55], dtype='int64', name='depth'))
spec = xrft.power_spectrum(
uniform,
dim=(S.cf.axes["Z"][0], "time"),
window="hann",
window_correction=True,
)
sym = (
2
* 2
* spec.isel(
freq_depth=slice(uniform.cf.sizes["Z"] // 2 + 1, None),
freq_time=slice(uniform.cf.sizes["time"] // 2 + 1, None),
)
)
sym.mean("freq_depth").rolling(freq_time=5).mean(center=True, min_periods=5).plot(
xscale="log", yscale="log"
)
[<matplotlib.lines.Line2D object at 0x2b8bababd5a0>]
import dcpy.ts
sym.plot(
xscale="log",
yscale="log",
norm=mpl.colors.LogNorm(),
robust=True,
cmap=mpl.cm.turbo,
)
Vertical wavenumber spectra#
think about vertical migration of EUC
ds = tree["kpp.lmd.004"].ds
ds.S2.sel(time=ds.time.dt.month.isin([10, 11]))
<xarray.DataArray 'S2' (time: 36600, zi: 27)>
dask.array<chunksize=(1464, 26), meta=np.ndarray>
Coordinates: (12/14)
* time (time) datetime64[ns] 2003-10-01T00:30:00 ... 2027-11-30...
xh float64 -140.0
yh float64 0.0625
yq float64 -0.0625
* zi (zi) float64 -230.8 -212.0 -194.4 -177.8 ... -5.0 -2.5 -0.0
eucmax (time) float64 dask.array<chunksize=(1464,), meta=np.ndarray>
... ...
oni (time) float32 0.2936 0.2936 0.2936 0.2936 ... nan nan nan
en_mask (time) bool False False False False ... False False False
ln_mask (time) bool False False False False ... False False False
warm_mask (time) bool True True True True ... True True True True
cool_mask (time) bool False False False False ... False False False
enso_transition (time) <U12 '____________' ... '____________'
Attributes:
long_name: $S^2$
units: s$^{-2}$- time: 36600
- zi: 27
- dask.array<chunksize=(1464, 26), meta=np.ndarray>
Array Chunk Bytes 3.77 MiB 148.69 kiB Shape (36600, 27) (1464, 26) Dask graph 50 chunks in 51 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]2003-10-01T00:30:00 ... 2027-11-...
array(['2003-10-01T00:30:00.000000000', '2003-10-01T01:30:00.000000000', '2003-10-01T02:30:00.000000000', ..., '2027-11-30T21:30:00.000000000', '2027-11-30T22:30:00.000000000', '2027-11-30T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - eucmax(time)float64dask.array<chunksize=(1464,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 285.94 kiB 11.44 kiB Shape (36600,) (1464,) Dask graph 25 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(1464,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 285.94 kiB 11.44 kiB Shape (36600,) (1464,) Dask graph 25 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 1464), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 0.94 MiB 38.60 kiB Shape (27, 36600) (27, 1464) Dask graph 25 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float320.2936 0.2936 0.2936 ... nan nan
- long_name :
- ONI
- units :
- degC
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- oceanic_nino_index
array([0.2935613, 0.2935613, 0.2935613, ..., nan, nan, nan], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... '____________'
- long_name :
- ONI
- units :
- degC
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- cell_measures :
- area: areacello
- time_avg_info :
- average_T1,average_T2,average_DT
- standard_name :
- oceanic_nino_index
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12')
- timePandasIndex
PandasIndex(DatetimeIndex(['2003-10-01 00:30:00', '2003-10-01 01:30:00', '2003-10-01 02:30:00', '2003-10-01 03:30:00', '2003-10-01 04:30:00', '2003-10-01 05:30:00', '2003-10-01 06:30:00', '2003-10-01 07:30:00', '2003-10-01 08:30:00', '2003-10-01 09:30:00', ... '2027-11-30 14:30:00', '2027-11-30 15:30:00', '2027-11-30 16:30:00', '2027-11-30 17:30:00', '2027-11-30 18:30:00', '2027-11-30 19:30:00', '2027-11-30 20:30:00', '2027-11-30 21:30:00', '2027-11-30 22:30:00', '2027-11-30 23:30:00'], dtype='datetime64[ns]', name='time', length=36600, freq=None)) - ziPandasIndex
PandasIndex(Float64Index([-230.77999999999997, -212.01999999999998, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.099999999999994, -24.809999999999995, -20.159999999999997, -16.15, -12.77, -10.0, -7.5, -5.0, -2.5, -0.0], dtype='float64', name='zi'))
- long_name :
- $S^2$
- units :
- s$^{-2}$
# f, ax = plt.subplots(1, 2, sharey=True, squeeze=False)
handles = []
for i, name in enumerate(tree.children.keys()):
ds = tree[name].ds.reset_coords()
u = ds.cf["sea_water_x_velocity"].load()
v = ds.cf["sea_water_y_velocity"].load()
S = (
(u.cf.differentiate("Z") + 1j * v.cf.differentiate("Z"))
.cf.sel(Z=slice(-200, 0))
.cf.interp(Z=np.arange(-105, -52, 5))
)
Zname = S.cf.axes["Z"][0]
spec = xrft.power_spectrum(
S,
dim=S.cf.axes["Z"],
window="hann",
window_correction=True,
).mean("time")
sym = 2 * spec.isel({f"freq_{Zname}": slice(S.cf.sizes["Z"] // 2 + 1, None)})
sym.name = "spectral density"
handles.append(sym.hvplot.line(logx=True, logy=True, label=name))
hv.Overlay(handles).opts(legend_position="right")
# f, ax = plt.subplots(1, 2, sharey=True, squeeze=False)
handles = []
for i, name in enumerate(tree.children.keys()):
ds = tree[name].ds.reset_coords()
u = ds.cf["sea_water_x_velocity"].load()
v = ds.cf["sea_water_y_velocity"].load()
S = (
(u.cf.differentiate("Z") + 1j * v.cf.differentiate("Z"))
.cf.sel(Z=slice(-200, 0))
.cf.interp(Z=np.arange(-105, -52, 5))
)
if "kpp" in name or "TAO" in name:
S = S.sel(time=ds.time.dt.month.isin([10, 11]))
Zname = S.cf.axes["Z"][0]
spec = xrft.power_spectrum(
S,
dim=S.cf.axes["Z"],
window="hann",
window_correction=True,
).mean("time")
sym = 2 * spec.isel({f"freq_{Zname}": slice(S.cf.sizes["Z"] // 2 + 1, None)})
sym.name = "spectral density"
handles.append(sym.hvplot.line(logx=True, logy=True, label=name))
hv.Overlay(handles).opts(legend_position="right")
f, ax = plt.subplots(1, 2, sharey=True, squeeze=False)
for name in tree.children.keys():
ds = tree[name].ds.reset_coords()
u = ds.cf["sea_water_x_velocity"].load()
v = ds.cf["sea_water_y_velocity"].load()
S = u.cf.differentiate("Z") + 1j * v.cf.differentiate("Z")
dcpy.ts.PlotSpectrum(
S.cf.sel(Z=-50, method="nearest").sel(time="2010"),
multitaper=False,
twoside=True,
nsmooth=12,
label=name,
lw=0.75,
ax=ax.ravel(),
)
ax.ravel()[0].set_ylim([1e-10, None])
ax.ravel()[-1].legend()
f.set_size_inches((8, 4))
Seasonal mean heat flux#
remapped.Jq.load()
<xarray.DataArray 'Jq' (time: 174000, zeuc: 100)>
array([[ nan, nan, -0.07841657, ..., nan,
nan, nan],
[ nan, nan, -0.07973828, ..., nan,
nan, nan],
[ nan, nan, -0.08094554, ..., nan,
nan, nan],
...,
[ nan, -0.07447129, nan, ..., nan,
nan, nan],
[ nan, -0.07471326, nan, ..., nan,
nan, nan],
[ nan, -0.07509062, nan, ..., nan,
nan, nan]])
Coordinates:
latitude float64 0.025
longitude float64 -140.0
* time (time) datetime64[ns] 1998-12-31T18:00:00 ... 2018-11-06T17:00:00
* zeuc (zeuc) float64 -247.5 -242.5 -237.5 -232.5 ... 237.5 242.5 247.5- time: 174000
- zeuc: 100
- nan nan -0.07842 nan -0.06425 nan -0.05399 ... nan nan nan nan nan nan
array([[ nan, nan, -0.07841657, ..., nan, nan, nan], [ nan, nan, -0.07973828, ..., nan, nan, nan], [ nan, nan, -0.08094554, ..., nan, nan, nan], ..., [ nan, -0.07447129, nan, ..., nan, nan, nan], [ nan, -0.07471326, nan, ..., nan, nan, nan], [ nan, -0.07509062, nan, ..., nan, nan, nan]]) - latitude()float640.025
array(0.025)
- longitude()float64-140.0
array(-140.0249939)
- time(time)datetime64[ns]1998-12-31T18:00:00 ... 2018-11-...
array(['1998-12-31T18:00:00.000000000', '1998-12-31T19:00:00.000000000', '1998-12-31T20:00:00.000000000', ..., '2018-11-06T15:00:32.000000000', '2018-11-06T16:00:16.000000000', '2018-11-06T17:00:00.000000000'], dtype='datetime64[ns]') - zeuc(zeuc)float64-247.5 -242.5 ... 242.5 247.5
- axis :
- Z
- units :
- m
array([-247.5, -242.5, -237.5, -232.5, -227.5, -222.5, -217.5, -212.5, -207.5, -202.5, -197.5, -192.5, -187.5, -182.5, -177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5, -137.5, -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5, -97.5, -92.5, -87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5, -7.5, -2.5, 2.5, 7.5, 12.5, 17.5, 22.5, 27.5, 32.5, 37.5, 42.5, 47.5, 52.5, 57.5, 62.5, 67.5, 72.5, 77.5, 82.5, 87.5, 92.5, 97.5, 102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5, 152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5])
(
remapped.Jq.groupby("time.season")
.mean()
.reindex(season=["DJF", "MAM", "JJA", "SON"])
.cf.plot.line(col="season")
)
<xarray.plot.facetgrid.FacetGrid>
Below EUC mixing#
as a test for the background mixing params.
overflows
acc , gs
DWBC
ideal age metric?
vertically integrated heat budget term, global map
import os
import datatree
micro_zeuc = datatree.open_datatree(
os.path.expanduser("~/datasets/microstructure/equix-tiwe-zeuc.average.nc")
).load()
for nodename, node in micro_zeuc.children.items():
node["u"].attrs["standard_name"] = "sea_water_x_velocity"
node["v"].attrs["standard_name"] = "sea_water_y_velocity"
node["KT"].attrs["standard_name"] = "ocean_vertical_heat_diffusivity"
node["ν"].attrs["standard_name"] = "ocean_vertical_momentum_diffusivity"
del node["Rig_T"].attrs["standard_name"]
del node["N2T"].attrs["standard_name"]
# euc_mean[nodename] = node
%autoreload
newtree = mixpods.bin_to_euc_centered_coordinate(tree)
for nodename, _ in tree.children.items():
tree[f"{nodename}/euc"] = newtree[f"{nodename}/euc"]
euc_mean = mixpods.average_euc(tree)
euc_mean.load()
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- zeuc: 50
- latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- reference_pressure()int640
array(0)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- u(zeuc)float32nan nan nan ... -0.2604 -0.3176 nan
- FORTRAN_format :
- epic_code :
- 1205
- generic_name :
- u
- long_name :
- u
- name :
- u
- standard_name :
- sea_water_x_velocity
- units :
- m/s
array([ nan, nan, nan, -0.34412488, -0.18681769, -0.13121113, -0.13110007, -0.13295907, -0.11932758, -0.08944993, -0.06136366, -0.02866847, 0.00694463, 0.04042118, 0.0743546 , 0.10901111, 0.14541332, 0.18769473, 0.23589374, 0.2907243 , 0.3542546 , 0.42696685, 0.50931793, 0.6012157 , 0.7011007 , 0.8074448 , 0.91773796, 1.0287796 , 1.1347147 , 1.2393551 , 1.2138937 , 1.0788711 , 0.91834927, 0.70995677, 0.5181305 , 0.3438547 , 0.18748318, 0.05523353, -0.04432773, -0.11947555, -0.17376517, -0.21139875, -0.24530154, -0.270233 , -0.24724326, -0.17648801, -0.07476283, -0.26038134, -0.31755555, nan], dtype=float32) - v(zeuc)float32nan nan nan ... 0.02461 nan
- FORTRAN_format :
- epic_code :
- 1206
- generic_name :
- v
- long_name :
- v
- name :
- v
- standard_name :
- sea_water_y_velocity
- units :
- m/s
array([ nan, nan, nan, 0.04195916, 0.05380342, 0.01274851, 0.00227688, -0.00924548, -0.01995938, -0.01586557, -0.01145717, -0.00865888, -0.00767306, -0.00643414, -0.00592971, -0.004652 , -0.00347026, -0.00338077, -0.00492731, -0.00728909, -0.00941122, -0.01004394, -0.00936652, -0.00851536, -0.00894195, -0.01160023, -0.01552894, -0.01913672, -0.02083625, -0.02033128, -0.01806714, -0.02482096, -0.03544367, -0.03519909, -0.03364132, -0.03147318, -0.030247 , -0.02728803, -0.02601177, -0.02645807, -0.02617701, -0.02455281, -0.02730868, -0.0510966 , -0.04896826, -0.01222637, -0.04429276, -0.02813384, 0.02461111, nan], dtype=float32) - theta(zeuc)float64nan 11.76 11.79 ... 27.16 27.48
- description :
- potential temperature using T, S=35
- long_name :
- $θ$
- standard_name :
- sea_water_potential_temperature
- units :
- degC
array([ nan, 11.75736668, 11.79488486, 11.84763801, 11.93816873, 12.05439489, 12.13125832, 12.06222168, 11.96635654, 11.9495776 , 11.98335018, 12.04190823, 12.1266774 , 12.23353507, 12.36732257, 12.53790349, 12.74391642, 12.98199528, 13.24475614, 13.53098288, 13.84172953, 14.17438081, 14.55655615, 15.0141248 , 15.57278846, 16.24306478, 17.04445091, 17.97797458, 19.09056328, 20.27529103, 21.49486726, 22.6751356 , 23.67650756, 24.39508511, 24.92568977, 25.29087219, 25.54098213, 25.70651579, 25.81024759, 25.87502808, 25.94671359, 26.06974854, 26.271824 , 26.51445195, 26.76821484, 26.96498047, 27.0757443 , 27.26711222, 27.1600157 , 27.47678427]) - S2(zeuc)float32nan nan nan ... 3.295e-05 nan
- long_name :
- $S^2$
array([ nan, nan, nan, 4.00262506e-05, 1.99538299e-05, 2.05517135e-05, 2.50414141e-05, 2.88889587e-05, 2.94069741e-05, 2.84150774e-05, 2.68097574e-05, 2.58364780e-05, 2.66908228e-05, 2.83490008e-05, 3.06443253e-05, 3.34476608e-05, 3.77655488e-05, 4.43660574e-05, 5.27910342e-05, 6.30350623e-05, 7.58075330e-05, 9.16752906e-05, 1.10956047e-04, 1.29934648e-04, 1.48486783e-04, 1.66213635e-04, 1.79030249e-04, 1.85601544e-04, 1.88380363e-04, 1.38660631e-04, 1.41002165e-04, 5.10490616e-04, 5.97267004e-04, 5.10636659e-04, 4.25692560e-04, 3.60526814e-04, 3.14358331e-04, 2.77291780e-04, 2.28823061e-04, 1.92833613e-04, 1.59598058e-04, 1.22315978e-04, 1.07536085e-04, 9.86665691e-05, 8.83758039e-05, 4.32099259e-05, 3.16419428e-05, 3.62538267e-05, 3.29506183e-05, nan], dtype=float32) - Rig_T(zeuc)float64nan nan nan ... 0.09194 0.03173 nan
- long_name :
- $Ri^g_T$
array([ nan, nan, nan, nan, nan, 0.01489311, 0.03165001, 0.06313003, 0.14558223, 0.30405107, 0.5510997 , 0.84501221, 1.00159469, 1.10171097, 1.17564499, 1.25644404, 1.32506321, 1.30966974, 1.20444936, 1.09169364, 1.01902732, 0.98161728, 0.93856368, 0.95847886, 1.0277866 , 1.14108322, 1.30324548, 1.53638172, 1.83930923, 3.08740971, 5.92696916, 0.64201333, 0.39953276, 0.31802397, 0.27029139, 0.2428626 , 0.22293594, 0.2077806 , 0.19648165, 0.18172446, 0.16687778, 0.15812352, 0.15209324, 0.14781051, 0.16795355, 0.18188397, 0.11292903, 0.09193901, 0.03173482, nan]) - Tflx_dia_diff(zeuc)float64nan nan nan nan ... nan nan nan
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -5.17514235e-07, nan, -2.82599747e-06, -3.66443945e-07, -6.14955296e-06, -3.76138545e-06, -2.92809353e-06, -6.45150609e-06, -9.99721035e-06, -1.10884138e-05, -1.18222800e-05, -1.43206993e-05, -1.53016769e-05, -1.85974551e-05, -1.64466029e-05, -1.42208123e-05, -1.48474476e-05, -1.59149521e-05, -1.12224783e-05, -7.01141757e-06, -7.11490467e-06, -4.62302402e-06, -3.49574049e-06, -4.14221714e-06, nan, nan, nan]) - KT(zeuc)float64nan nan nan nan ... nan nan nan
- standard_name :
- ocean_vertical_heat_diffusivity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 2.91855685e-05, nan, 2.63724818e-04, 5.17551881e-05, 5.16996775e-04, 2.38649440e-03, 4.78397845e-04, 4.52111916e-04, 6.96026866e-04, 9.52619969e-04, 1.08718966e-03, 1.55516006e-03, 2.27080410e-02, 2.70914629e-02, 6.55873581e-03, 4.65641779e-03, 2.04377327e-02, 2.38048398e-02, 1.97063766e-02, 2.76059209e-03, 2.76672216e-03, 2.04669252e-03, 2.47503727e-03, 2.86957564e-03, nan, nan, nan]) - ν(zeuc)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_momentum_diffusivity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.68499682e-05, 3.85379335e-05, 8.95743212e-04, 6.61870626e-04, 1.25469635e-03, 1.04469512e-03, 4.82821120e-04, 6.60574882e-04, 1.36210951e-03, 9.19967017e-04, 1.06437092e-03, 1.72836434e-03, 2.17503465e-03, 2.81499143e-03, 4.14499709e-03, 1.75873300e-02, 6.07257448e-03, 6.30236566e-03, 1.63667027e-02, 2.24646176e-03, 4.97174757e-06, nan, nan, nan, nan]) - chi(zeuc)float64nan nan nan nan ... nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.86298805e-08, nan, 6.29706882e-08, 6.09176283e-08, 9.08369013e-07, 4.42442169e-07, 4.39236144e-07, 1.11900126e-06, 1.65520285e-06, 1.57006590e-06, 1.24562823e-06, 1.13238867e-06, 9.20620793e-07, 8.59863821e-07, 6.37894873e-07, 4.62336468e-07, 4.36265962e-07, 4.36628547e-07, 2.59989298e-07, 1.10137167e-07, 1.40344814e-07, 5.56811423e-08, 1.42741529e-08, 1.62351688e-08, nan, nan, nan]) - eps(zeuc)float64nan nan nan nan ... nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 7.84301387e-09, nan, 3.62689641e-08, 4.56603535e-09, 1.34903330e-07, 8.03762811e-08, 6.24810991e-08, 1.41756879e-07, 2.40822512e-07, 2.48318857e-07, 2.92684586e-07, 3.38003888e-07, 3.36031778e-07, 4.38201807e-07, 3.46936120e-07, 2.95196047e-07, 3.14040551e-07, 3.34615731e-07, 2.23966313e-07, 1.29571308e-07, 1.43364960e-07, 9.57843724e-08, 5.78129980e-08, 6.21678115e-08, nan, nan, nan])
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: latitude float32 0.0 longitude float32 -140.0 reference_pressure int64 0 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 182.5 192.5 Data variables: u (zeuc) float32 nan nan nan ... -0.2604 -0.3176 nan v (zeuc) float32 nan nan nan ... -0.02813 0.02461 nan theta (zeuc) float64 nan 11.76 11.79 ... 27.27 27.16 27.48 S2 (zeuc) float32 nan nan nan ... 3.625e-05 3.295e-05 nan Rig_T (zeuc) float64 nan nan nan nan ... 0.09194 0.03173 nan Tflx_dia_diff (zeuc) float64 nan nan nan nan ... nan nan nan KT (zeuc) float64 nan nan nan nan ... 0.00287 nan nan nan ν (zeuc) float64 nan nan nan nan nan ... nan nan nan nan chi (zeuc) float64 nan nan nan nan ... 1.624e-08 nan nan nan eps (zeuc) float64 nan nan nan nan ... 6.217e-08 nan nan nanTAO- zeuc: 50
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- Tflx_dia_diff(zeuc)float321.993e-08 2.055e-08 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([1.99337329e-08, 2.05541344e-08, 2.09820854e-08, 2.12172608e-08, 2.17732055e-08, 2.11404103e-08, 1.88170048e-08, 2.02018402e-08, 1.85675297e-08, 1.67737362e-08, 1.47612083e-08, 1.39845033e-08, 1.43860861e-08, 1.45533212e-08, 1.68728480e-08, 2.18248424e-08, 2.00118091e-08, 2.62560054e-08, 3.27239533e-08, 4.31971259e-08, 5.48310979e-08, 7.83728638e-08, 8.56392361e-08, 7.29909928e-08, 7.89940344e-08, 7.16221535e-08, 8.24730719e-08, 8.97363108e-08, 1.14854465e-07, 1.16268268e-07, 8.87113742e-08, 6.96811924e-07, 6.34010848e-06, 1.42570707e-05, 1.82244657e-05, 1.80941097e-05, 1.87573423e-05, 2.04298794e-05, 2.28651788e-05, 2.33484861e-05, 2.10828657e-05, 1.31928864e-05, 1.17865256e-05, 2.07541343e-05, 4.46153081e-06, nan, nan, nan, nan, nan], dtype=float32) - Kd_heat(zeuc)float321.001e-06 1.001e-06 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_heat_diffusivity
array([1.0006705e-06, 1.0006337e-06, 1.0006618e-06, 1.0006443e-06, 1.0006701e-06, 1.0006614e-06, 1.0006444e-06, 1.0006701e-06, 1.0006628e-06, 1.0106939e-06, 1.0401673e-06, 1.0006507e-06, 1.1119579e-06, 1.2560798e-06, 1.4861683e-06, 1.6268899e-06, 1.3251024e-06, 1.4133223e-06, 1.2183413e-06, 1.3440165e-06, 1.3007792e-06, 2.1559247e-06, 1.7224424e-06, 1.4074643e-06, 1.1677428e-06, 1.0357805e-06, 1.0051077e-06, 1.0014580e-06, 1.0006426e-06, 1.0018749e-06, 1.1331898e-06, 4.9953865e-06, 1.3441498e-04, 5.2427029e-04, 1.1975758e-03, 2.1821284e-03, 4.7503118e-03, 8.6501129e-03, 1.4562671e-02, 1.8755380e-02, 1.9549852e-02, 1.4596995e-02, 1.4510026e-02, 2.2565287e-02, 6.9412617e-03, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float327.983e-10 8.5e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([7.98321798e-10, 8.50012727e-10, 8.84189610e-10, 9.11799580e-10, 9.56660640e-10, 9.04769315e-10, 7.18309079e-10, 8.29037838e-10, 7.05128622e-10, 5.75203885e-10, 4.42727882e-10, 4.47146736e-10, 4.10015882e-10, 3.96392363e-10, 4.70237904e-10, 7.21766591e-10, 7.88129451e-10, 1.20723875e-09, 2.13015827e-09, 3.33278893e-09, 5.25600186e-09, 7.23560190e-09, 9.75383418e-09, 8.95890473e-09, 1.27101201e-08, 1.15444925e-08, 1.67442575e-08, 2.00238528e-08, 2.98242746e-08, 3.13590256e-08, 1.84597369e-08, 2.74945506e-07, 1.06124980e-06, 1.65242091e-06, 1.08279119e-06, 7.10793756e-07, 4.89256706e-07, 3.81183526e-07, 3.18817285e-07, 3.70346726e-07, 4.42385783e-07, 5.16218051e-07, 4.75548546e-07, 3.79147195e-07, 8.43883186e-07, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float321.156e-11 2.178e-11 ... nan nan
- long_name :
- $SP$
- units :
- W/kg
array([1.15639529e-11, 2.17766170e-11, 1.74849232e-11, 4.56000897e-11, 2.65874944e-11, 3.11748995e-11, 6.49077944e-11, 3.82845249e-11, 6.73043704e-11, 8.56044610e-11, 2.07101267e-10, 5.38141032e-10, 2.82579654e-10, 6.71621370e-10, 9.14939069e-10, 1.74195014e-09, 2.41823384e-09, 3.77422449e-09, 4.95988628e-09, 7.43976303e-09, 7.97446198e-09, 1.05372608e-08, 1.19248069e-08, 1.35605447e-08, 1.49778963e-08, 1.52103485e-08, 1.40317695e-08, 1.32568365e-08, 9.80185533e-09, 2.25747043e-09, 3.73590581e-09, 1.40937743e-07, 1.63201705e-07, 2.26533999e-07, 1.88171967e-07, 2.71855839e-07, 3.33268503e-07, 3.42996486e-07, 3.06429854e-07, 2.85460573e-07, 3.19302416e-07, 4.04349180e-07, 3.96060244e-07, 2.73132542e-07, 4.41750558e-07, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float322.532e-07 3.085e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([2.53197157e-07, 3.08510607e-07, 2.92727435e-07, 4.32506539e-07, 3.44354930e-07, 3.59136124e-07, 5.08331141e-07, 3.83767656e-07, 5.07877189e-07, 5.82732639e-07, 1.15930766e-06, 2.78240032e-06, 1.53309384e-06, 3.44462319e-06, 4.66998154e-06, 8.77538332e-06, 1.20398645e-05, 1.87341047e-05, 2.45930187e-05, 3.67302637e-05, 3.95981842e-05, 5.22509799e-05, 5.93275399e-05, 6.70128939e-05, 7.40271207e-05, 7.50657564e-05, 6.94298578e-05, 6.57120399e-05, 4.91650717e-05, 1.24117068e-05, 1.86605121e-05, 3.53242329e-04, 3.78591998e-04, 3.90216097e-04, 1.84304707e-04, 1.55016198e-04, 1.40105636e-04, 1.20296543e-04, 9.45677166e-05, 8.90597657e-05, 9.51775655e-05, 1.15308751e-04, 1.09259403e-04, 7.42343545e-05, 1.64657977e-04, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32215.0 202.1 206.4 ... nan nan nan
- long_name :
- $Ri^g_T$
array([2.1503494e+02, 2.0214403e+02, 2.0636514e+02, 2.0012900e+02, 1.8109155e+02, 2.0417029e+02, 1.3411453e+02, 1.9008124e+02, 1.5265700e+02, 1.5746408e+02, 4.6662010e+01, 1.1054462e+01, 2.8667643e+01, 8.2051058e+00, 5.2001052e+00, 3.1951575e+00, 2.6567822e+00, 2.2889338e+00, 2.2457385e+00, 1.9907213e+00, 2.3420768e+00, 2.0011907e+00, 1.9492234e+00, 1.8765500e+00, 2.0379128e+00, 2.0180886e+00, 2.5594373e+00, 2.9622624e+00, 5.6772432e+00, 3.1978878e+01, 1.7669277e+01, 1.5686954e+00, 1.0714781e+00, 5.2440548e-01, 3.9277917e-01, 2.9803014e-01, 2.4751794e-01, 2.4547334e-01, 3.0760974e-01, 3.3345494e-01, 3.1290802e-01, 1.8050675e-01, 1.9237757e-01, 2.6489717e-01, 1.3896098e-02, nan, nan, nan, nan, nan], dtype=float32) - uo(zeuc)float320.005257 -0.0184 0.0179 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([ 0.00525669, -0.01840188, 0.01790354, 0.0044428 , -0.0186389 , 0.01406885, 0.0060294 , -0.01403717, 0.00815873, 0.00842694, 0.01887635, 0.0139282 , 0.02721404, 0.03995026, 0.0621833 , 0.08161839, 0.11901507, 0.14735334, 0.20004503, 0.23976822, 0.32848406, 0.3751118 , 0.4712556 , 0.52341986, 0.5774437 , 0.7075572 , 0.77591175, 0.8743804 , 0.9184269 , 1.0187924 , 0.9954761 , 0.9605037 , 0.7945212 , 0.57813746, 0.44689173, 0.33381617, 0.23095536, 0.12913482, 0.03992718, -0.02481052, -0.08560526, -0.15248425, -0.10183207, -0.08354276, -0.13608389, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float32-0.00127 1.53e-05 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-1.2702544e-03, 1.5298558e-05, 1.3026360e-03, -1.1253332e-03, -8.5902464e-04, 1.3367438e-03, -1.2649365e-03, -1.2388808e-03, 6.0991739e-04, -3.4798661e-05, 3.7417454e-03, -6.4203995e-03, -1.3410304e-04, 7.5575250e-04, -1.5261871e-03, -8.9544582e-04, -2.8911787e-03, -2.2536672e-03, -3.4135813e-03, -3.5160836e-03, -4.5007500e-03, -5.0586360e-03, -4.4486639e-03, -5.7875998e-03, -5.1613864e-03, -6.5229321e-03, -6.9063683e-03, -7.4243234e-03, -5.4123211e-03, -3.4078853e-03, -7.4119419e-03, -8.3914027e-03, -2.5121963e-03, -3.6355543e-03, -1.0095604e-02, -1.7872766e-02, -2.3629947e-02, -2.7661886e-02, -2.9444154e-02, -2.7525136e-02, -2.4786126e-02, -1.9260338e-02, -1.8448964e-02, -1.3063808e-01, -1.2816702e-01, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float320.0002048 0.0002048 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
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- cell_measures :
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- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
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- units :
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array([10.102968 , 10.372348 , 10.466407 , 10.773228 , 11.006616 , 11.139993 , 11.4113245, 11.583537 , 11.776898 , 11.990218 , 12.162569 , 12.333334 , 12.339282 , 12.497538 , 12.593491 , 12.730412 , 12.862454 , 13.050186 , 13.259371 , 13.581723 , 14.052092 , 14.471857 , 15.148092 , 15.4848175, 16.043379 , 16.859596 , 17.563826 , 18.593836 , 19.781422 , 20.625542 , 21.567474 , 22.509794 , 23.978546 , 25.125193 , 25.493336 , 25.699633 , 25.858055 , 25.986782 , 26.08607 , 26.141712 , 26.089527 , 25.978794 , 26.172031 , 25.693325 , 25.745844 , nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: yh float64 0.0625 yq float64 -0.0625 xh float64 -140.0 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: Tflx_dia_diff (zeuc) float32 1.993e-08 2.055e-08 2.098e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 7.983e-10 8.5e-10 8.842e-10 ... nan nan nan eps (zeuc) float32 1.156e-11 2.178e-11 1.748e-11 ... nan nan nan S2 (zeuc) float32 2.532e-07 3.085e-07 2.927e-07 ... nan nan nan Rig_T (zeuc) float32 215.0 202.1 206.4 200.1 ... nan nan nan nan uo (zeuc) float32 0.005257 -0.0184 0.0179 ... nan nan nan vo (zeuc) float32 -0.00127 1.53e-05 0.001303 ... nan nan nan ν (zeuc) float32 0.0002048 0.0002048 0.0002047 ... nan nan nan thetao (zeuc) float32 10.1 10.37 10.47 10.77 ... nan nan nan nanbaseline- zeuc: 50
- yh()float640.0625
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- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
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array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
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- domain_decomposition :
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- long_name :
- q point nominal latitude
- units :
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array(-0.06249997)
- xh()float64-140.0
- cartesian_axis :
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- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- Tflx_dia_diff(zeuc)float322.117e-08 2.133e-08 ... nan nan
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- area: areacello
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- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
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- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
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- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([1.0006451e-06, 1.0006506e-06, 1.0006515e-06, 1.0006414e-06, 1.0006618e-06, 1.0006513e-06, 1.0006434e-06, 1.0006655e-06, 1.0176084e-06, 1.0149389e-06, 1.0019072e-06, 1.2137010e-06, 1.0805032e-06, 1.4297108e-06, 1.4507829e-06, 1.1778233e-06, 1.1204596e-06, 1.1991365e-06, 1.0945305e-06, 1.0956771e-06, 1.1185289e-06, 1.2680930e-06, 1.1178845e-06, 1.0855989e-06, 1.0786908e-06, 1.0059720e-06, 1.0019743e-06, 1.0011390e-06, 1.0006572e-06, 1.0011006e-06, 1.1255088e-06, 1.8804314e-05, 1.1712696e-04, 4.8406073e-04, 1.1037614e-03, 3.1598986e-03, 6.9173714e-03, 1.1358041e-02, 1.3600050e-02, 1.3077446e-02, 1.2642518e-02, 1.0288579e-02, 1.1792418e-02, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float329.004e-10 9.152e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([9.0043989e-10, 9.1522040e-10, 1.0396450e-09, 9.1001395e-10, 9.6060004e-10, 9.5469777e-10, 7.0223621e-10, 7.3680501e-10, 5.9830035e-10, 4.7491955e-10, 3.9812650e-10, 4.7236026e-10, 3.7199399e-10, 5.7536242e-10, 6.8126182e-10, 7.5827411e-10, 1.0768707e-09, 1.8212872e-09, 2.5872873e-09, 4.2573043e-09, 5.7363847e-09, 7.2878090e-09, 8.2911358e-09, 9.9289998e-09, 1.2009678e-08, 1.5639399e-08, 1.8246034e-08, 1.9992989e-08, 2.9070740e-08, 3.2245666e-08, 1.5850427e-08, 3.6572640e-08, 1.2725955e-06, 1.6085360e-06, 1.2024104e-06, 7.9952855e-07, 5.2454573e-07, 4.2225017e-07, 4.9829407e-07, 5.8574591e-07, 4.9689334e-07, 5.1183719e-07, 4.9258375e-07, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float323.59e-11 4.422e-11 ... nan nan
- long_name :
- $SP$
- units :
- W/kg
array([3.59027183e-11, 4.42174838e-11, 2.77531192e-11, 7.80116666e-11, 5.06235574e-11, 2.85473919e-11, 1.16416266e-10, 7.47296877e-11, 1.13882292e-10, 1.71752043e-10, 1.49682516e-10, 6.45378861e-10, 5.82192239e-10, 9.41143496e-10, 1.53500745e-09, 2.32454345e-09, 3.29866312e-09, 4.84144280e-09, 6.77605083e-09, 8.78367334e-09, 1.04637676e-08, 1.21881625e-08, 1.36058320e-08, 1.52217066e-08, 1.58607776e-08, 1.56801701e-08, 1.56176441e-08, 1.30347049e-08, 8.60564153e-09, 1.97380490e-09, 3.19046922e-09, 9.38489038e-08, 2.58067558e-07, 2.76905070e-07, 3.83082266e-07, 4.22829004e-07, 4.42603323e-07, 4.26097785e-07, 4.30012506e-07, 4.63429330e-07, 4.42184643e-07, 4.32148340e-07, 4.48803803e-07, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float323.838e-07 4.264e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([3.8381995e-07, 4.2644211e-07, 3.6150070e-07, 5.9201142e-07, 4.6376638e-07, 3.5570309e-07, 7.5283435e-07, 5.5199490e-07, 7.2743495e-07, 9.9200213e-07, 8.6687038e-07, 3.3345341e-06, 2.9840382e-06, 4.8193274e-06, 7.7370651e-06, 1.1557462e-05, 1.6346772e-05, 2.3976709e-05, 3.3468012e-05, 4.3415948e-05, 5.1736562e-05, 6.0332572e-05, 6.7232111e-05, 7.5159194e-05, 7.8313511e-05, 7.7508535e-05, 7.7205652e-05, 6.4623826e-05, 4.3322329e-05, 1.1143856e-05, 1.5413378e-05, 2.9862832e-04, 6.0351175e-04, 4.6424806e-04, 3.6171466e-04, 2.8594036e-04, 2.1869803e-04, 1.7350054e-04, 1.7688426e-04, 1.9089431e-04, 1.7569039e-04, 1.9307368e-04, 1.8426294e-04, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32177.0 140.2 155.8 ... nan nan nan
- long_name :
- $Ri^g_T$
array([1.7700241e+02, 1.4017377e+02, 1.5582840e+02, 1.4044011e+02, 1.5148082e+02, 1.7303912e+02, 1.1180737e+02, 1.4587625e+02, 1.2738346e+02, 6.4905960e+01, 4.9831394e+01, 8.3011351e+00, 1.0125983e+01, 5.7563944e+00, 3.7067041e+00, 2.7272551e+00, 2.4012811e+00, 2.1475210e+00, 1.9123729e+00, 1.9800160e+00, 2.0326688e+00, 1.8785818e+00, 1.8993816e+00, 1.8516837e+00, 1.9699457e+00, 2.4552357e+00, 2.5303655e+00, 3.2691784e+00, 6.8624544e+00, 3.8340775e+01, 2.2407738e+01, 1.0736854e+00, 4.4377226e-01, 3.1085035e-01, 2.2195560e-01, 1.8700995e-01, 1.9031787e-01, 2.2211176e-01, 2.2589630e-01, 1.9110769e-01, 1.7184874e-01, 1.3849184e-01, 2.1321297e-01, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - uo(zeuc)float320.004048 0.01676 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([ 0.00404828, 0.01676483, 0.0061307 , 0.00345092, 0.01299025, 0.00860957, 0.00583755, 0.00975394, 0.01646413, 0.0206379 , 0.03018612, 0.03431475, 0.04300458, 0.06420571, 0.08346127, 0.11615828, 0.14630534, 0.19210795, 0.23916784, 0.3171235 , 0.3691892 , 0.44943303, 0.5751361 , 0.6041893 , 0.71150935, 0.78162134, 0.87785274, 0.95903206, 1.0309594 , 1.0698215 , 1.0845604 , 1.0534542 , 0.89380115, 0.6437762 , 0.46004242, 0.30624336, 0.16267328, 0.04375282, -0.04332588, -0.1130536 , -0.17761762, -0.13251486, 0.0162907 , nan, nan, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float320.001048 0.002618 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
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- cell_methods :
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- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([0.00020475, 0.00020475, 0.00020479, 0.00020481, 0.00020481, 0.00020482, 0.00020483, 0.00020483, 0.00020483, 0.00020485, 0.00020483, 0.00020489, 0.00020488, 0.00020487, 0.00020488, 0.00020491, 0.00020493, 0.00020495, 0.00020495, 0.00020496, 0.00020496, 0.00020498, 0.000205 , 0.00020501, 0.00020503, 0.00020506, 0.0002051 , 0.00020513, 0.00020516, 0.00020519, 0.00020521, 0.00022332, 0.00031059, 0.00066837, 0.00124214, 0.00244679, 0.00480632, 0.00820587, 0.00956165, 0.01016004, 0.00855367, 0.00905411, 0.00920703, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float3210.4 10.54 10.86 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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array([10.40172 , 10.537823 , 10.859438 , 11.050811 , 11.223505 , 11.51517 , 11.68402 , 11.896485 , 12.074736 , 12.329481 , 12.410086 , 12.484327 , 12.630804 , 12.72751 , 12.877808 , 12.997511 , 13.182478 , 13.378202 , 13.6936865, 14.069012 , 14.489995 , 15.146014 , 15.748816 , 16.187355 , 17.096003 , 17.545921 , 18.504902 , 19.545824 , 20.847746 , 21.178389 , 22.544098 , 23.383211 , 24.294075 , 25.223984 , 25.63944 , 25.85893 , 26.019669 , 26.134165 , 26.18872 , 26.17477 , 26.322565 , 26.62483 , 26.912878 , nan, nan, nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: yh float64 0.0625 yq float64 -0.0625 xh float64 -140.0 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: Tflx_dia_diff (zeuc) float32 2.117e-08 2.133e-08 2.272e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 9.004e-10 9.152e-10 1.04e-09 ... nan nan nan eps (zeuc) float32 3.59e-11 4.422e-11 2.775e-11 ... nan nan nan S2 (zeuc) float32 3.838e-07 4.264e-07 3.615e-07 ... nan nan nan Rig_T (zeuc) float32 177.0 140.2 155.8 140.4 ... nan nan nan nan uo (zeuc) float32 0.004048 0.01676 0.006131 ... nan nan nan vo (zeuc) float32 0.001048 0.002618 -0.001801 ... nan nan nan ν (zeuc) float32 0.0002048 0.0002048 0.0002048 ... nan nan nan thetao (zeuc) float32 10.4 10.54 10.86 11.05 ... nan nan nan nankpp.lmd.004- zeuc: 50
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- axis :
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- domain_decomposition :
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- long_name :
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- yq()float64-0.0625
- axis :
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- long_name :
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- domain_decomposition :
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- long_name :
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- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- Tflx_dia_diff(zeuc)float321.755e-08 1.924e-08 ... nan nan
- cell_measures :
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- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
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- time_avg_info :
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- units :
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- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([1.00069019e-06, 1.00065347e-06, 1.00067587e-06, 1.00063551e-06, 1.00067132e-06, 1.00066575e-06, 1.00063346e-06, 1.00066700e-06, 1.00066563e-06, 1.00066734e-06, 1.00066416e-06, 1.00062528e-06, 1.00066745e-06, 1.00067314e-06, 1.00482680e-06, 1.07401365e-06, 1.08523659e-06, 1.39880501e-06, 4.77538333e-06, 2.90892385e-05, 4.31792432e-05, 6.99554439e-05, 9.46688160e-05, 9.24607375e-05, 7.00155651e-05, 5.26795411e-05, 1.31007955e-05, 5.59393993e-06, 2.53372355e-06, 1.77613265e-06, 1.33658182e-06, 2.10412065e-04, 3.71373346e-04, 8.84195731e-04, 1.33982731e-03, 2.16023228e-03, 4.38748859e-03, 7.71976588e-03, 1.28700137e-02, 1.76899210e-02, 1.95113998e-02, 1.61320884e-02, 1.55900959e-02, 2.49941684e-02, 7.19807623e-03, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float326.202e-10 7.44e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([6.2018657e-10, 7.4400919e-10, 7.4519668e-10, 1.0408558e-09, 8.8766766e-10, 1.0477264e-09, 1.3862269e-09, 1.1847184e-09, 1.3015795e-09, 1.2918184e-09, 1.2502051e-09, 1.0106910e-09, 1.1095831e-09, 9.3900832e-10, 7.7561230e-10, 6.9214173e-10, 6.8843903e-10, 1.1982592e-09, 3.6828227e-09, 1.5802890e-08, 3.6510770e-08, 7.2088909e-08, 1.4925237e-07, 2.0199380e-07, 1.9517613e-07, 1.8122286e-07, 1.1386960e-07, 1.1739417e-07, 6.3101886e-08, 2.3677337e-08, 7.7539383e-08, 2.8464769e-06, 1.7457008e-06, 1.9661063e-06, 1.1580179e-06, 8.1946200e-07, 5.9891721e-07, 4.6636660e-07, 3.9778465e-07, 3.8270457e-07, 4.3542067e-07, 4.8403251e-07, 4.3925988e-07, 4.1374929e-07, 6.2473862e-07, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float32-3.486e-11 -3.977e-11 ... nan nan
- long_name :
- $SP$
- units :
- W/kg
array([-3.4862623e-11, -3.9766645e-11, -3.8900622e-11, -4.7081988e-11, -4.3221985e-11, -4.7898751e-11, -5.5496788e-11, -5.1265801e-11, -5.3704884e-11, -5.2660064e-11, -5.0599188e-11, -4.7735437e-11, -4.6929866e-11, -4.2068782e-11, -3.7635922e-11, -3.1652236e-11, 2.6561841e-11, 4.2512421e-10, 1.1250213e-09, 1.1974399e-09, 1.2842949e-09, 1.0302516e-09, 7.0307132e-10, 5.3557042e-10, 2.8639111e-09, 3.1134342e-09, 3.8747410e-09, 3.7293511e-09, 2.5072611e-09, 4.3973367e-11, 3.5692384e-08, 2.4116966e-07, 1.7748782e-07, 1.3357842e-07, 1.8830977e-07, 2.7809878e-07, 3.2942989e-07, 3.3744183e-07, 3.1265549e-07, 2.6905587e-07, 2.6631113e-07, 3.3413107e-07, 3.7643218e-07, 2.1044070e-07, 4.7721613e-07, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float322.995e-07 2.884e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([2.99454200e-07, 2.88390282e-07, 3.94741505e-07, 5.30968293e-07, 4.65758774e-07, 5.12829388e-07, 5.89175841e-07, 6.17585215e-07, 6.72956560e-07, 8.13043584e-07, 7.72349836e-07, 6.12035194e-07, 6.96600068e-07, 7.94913888e-07, 7.87037322e-07, 1.80515713e-06, 4.14777469e-06, 1.13599381e-05, 2.49655386e-05, 3.86873107e-05, 5.25923242e-05, 6.51414230e-05, 8.23767332e-05, 1.08375476e-04, 1.27818377e-04, 1.52830122e-04, 1.93246451e-04, 2.14157830e-04, 2.23237090e-04, 5.20113535e-05, 1.84496806e-04, 8.95032368e-04, 6.05278881e-04, 2.61586159e-04, 1.78649512e-04, 1.69708088e-04, 1.52827561e-04, 1.26139494e-04, 1.06460036e-04, 9.21873580e-05, 9.53566050e-05, 1.12116184e-04, 1.05927014e-04, 7.23837584e-05, 1.64326877e-04, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32180.5 238.2 184.8 ... nan nan nan
- long_name :
- $Ri^g_T$
array([1.80487656e+02, 2.38210449e+02, 1.84815262e+02, 1.29452072e+02, 1.42724121e+02, 1.33232925e+02, 1.08492073e+02, 1.15649536e+02, 1.18179962e+02, 9.78238068e+01, 1.05259544e+02, 1.15983322e+02, 1.20559654e+02, 1.13338959e+02, 1.05069458e+02, 6.24187737e+01, 2.53599625e+01, 1.01569824e+01, 3.78092313e+00, 1.77504945e+00, 1.37038183e+00, 1.04456234e+00, 9.38269973e-01, 8.45975637e-01, 7.97548473e-01, 7.48805523e-01, 9.33551669e-01, 1.23177063e+00, 1.45686054e+00, 6.68316460e+00, 8.43145561e+00, 5.06967545e-01, 5.58340728e-01, 4.84673023e-01, 3.70436102e-01, 3.00139725e-01, 2.60080546e-01, 2.52664149e-01, 3.00420284e-01, 3.45842630e-01, 3.32556754e-01, 2.14251995e-01, 1.95444688e-01, 3.57639253e-01, 3.24461162e-02, nan, nan, nan, nan, nan], dtype=float32) - uo(zeuc)float320.02499 -0.01359 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([ 0.02499344, -0.01359064, 0.01056438, 0.02405857, -0.01237831, 0.01013298, 0.02191616, -0.00767961, 0.00422038, 0.00949787, 0.0014572 , 0.00401488, 0.01101161, 0.00240373, 0.01586246, 0.00856926, 0.03002909, 0.03372687, 0.07525551, 0.11013176, 0.18264885, 0.2575284 , 0.32371256, 0.41555193, 0.50246024, 0.6455064 , 0.75232065, 0.91769034, 1.0688589 , 1.1614565 , 1.1729106 , 1.0739865 , 0.78872025, 0.62034184, 0.49858826, 0.380375 , 0.27201495, 0.16802388, 0.07608254, 0.00831099, -0.04175798, -0.08453508, -0.04369244, 0.12923996, 0.08277461, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float320.001215 -0.0004066 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([ 0.00121472, -0.00040661, 0.00017783, 0.00129372, -0.00017322, 0.00046198, 0.00148334, -0.00020848, -0.00060944, 0.00136364, -0.00039485, -0.00453154, 0.00167347, -0.00063127, 0.00158471, -0.00220249, -0.00043014, -0.00388327, -0.0008026 , -0.00243983, 0.0007454 , 0.00054837, -0.00046543, -0.0001288 , -0.00214814, -0.00606339, -0.00876785, -0.00452488, -0.0027157 , -0.00483246, -0.01359488, -0.00209842, 0.00908873, 0.0029544 , -0.00833544, -0.01829557, -0.02495088, -0.02942124, -0.03080559, -0.02925389, -0.02618347, -0.02168874, -0.0189103 , -0.06369759, -0.05137211, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float325.046e-06 5.048e-06 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([5.0462340e-06, 5.0475892e-06, 5.0424624e-06, 5.0440090e-06, 5.0467970e-06, 5.0418971e-06, 5.0431631e-06, 5.0451308e-06, 5.0414542e-06, 5.0420358e-06, 5.0389685e-06, 5.0458352e-06, 5.0413701e-06, 5.0399844e-06, 5.0401809e-06, 5.0381868e-06, 5.1015418e-06, 6.8741460e-06, 1.5321362e-05, 3.2997450e-05, 5.5269622e-05, 8.3268496e-05, 6.9354384e-05, 9.5276482e-05, 7.8466102e-05, 4.6640664e-05, 3.3242406e-05, 1.1284424e-05, 5.3480094e-06, 1.8180930e-05, 8.6516111e-06, 7.5917036e-05, 4.6185430e-04, 9.8257314e-04, 1.4048299e-03, 2.0107760e-03, 3.2659161e-03, 5.8874162e-03, 9.1227721e-03, 1.1989832e-02, 1.2402624e-02, 1.1848887e-02, 1.1853820e-02, 1.4631215e-02, 3.4738344e-03, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float329.926 10.25 10.35 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([ 9.925618 , 10.254531 , 10.354003 , 10.53566 , 10.865824 , 10.970225 , 11.218256 , 11.542037 , 11.700399 , 12.0454 , 12.268132 , 12.709218 , 12.647938 , 12.898456 , 13.133925 , 13.318799 , 13.4945545, 13.652494 , 13.850175 , 14.109336 , 14.381747 , 14.7182045, 15.119492 , 15.392733 , 15.855298 , 16.483904 , 17.017002 , 18.144295 , 19.60642 , 20.326212 , 20.99324 , 22.676971 , 24.507181 , 25.129498 , 25.416185 , 25.618725 , 25.778563 , 25.90913 , 26.007828 , 26.056711 , 26.065672 , 26.052399 , 26.221981 , 26.439838 , 26.489485 , nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: yh float64 0.0625 yq float64 -0.0625 xh float64 -140.0 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: Tflx_dia_diff (zeuc) float32 1.755e-08 1.924e-08 1.924e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 6.202e-10 7.44e-10 7.452e-10 ... nan nan nan eps (zeuc) float32 -3.486e-11 -3.977e-11 -3.89e-11 ... nan nan S2 (zeuc) float32 2.995e-07 2.884e-07 3.947e-07 ... nan nan nan Rig_T (zeuc) float32 180.5 238.2 184.8 129.5 ... nan nan nan nan uo (zeuc) float32 0.02499 -0.01359 0.01056 ... nan nan nan vo (zeuc) float32 0.001215 -0.0004066 0.0001778 ... nan nan nan ν (zeuc) float32 5.046e-06 5.048e-06 5.042e-06 ... nan nan nan thetao (zeuc) float32 9.926 10.25 10.35 10.54 ... nan nan nan nannew_baseline.hb- zeuc: 50
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- Tflx_dia_diff(zeuc)float321.85e-08 1.913e-08 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
array([1.85005504e-08, 1.91293861e-08, 2.06160440e-08, 2.21359180e-08, 2.24070380e-08, 2.48522607e-08, 2.56552166e-08, 2.63386593e-08, 2.74371743e-08, 2.67701328e-08, 2.69359877e-08, 2.44756340e-08, 2.41357050e-08, 2.14560352e-08, 1.97716332e-08, 1.66414509e-08, 1.74895423e-08, 2.14079350e-08, 3.45732083e-08, 6.12063857e-08, 7.47684510e-08, 1.62360351e-07, 2.77199149e-07, 4.46485075e-07, 3.56709933e-07, 1.23152688e-07, 1.46308878e-07, 1.06570774e-07, 1.38309090e-07, 1.07144494e-07, 1.11251758e-07, 5.28505052e-06, 1.73781937e-05, 1.98480011e-05, 2.11431343e-05, 2.20121365e-05, 2.35071038e-05, 2.38630219e-05, 2.23098959e-05, 1.71911925e-05, 1.30739172e-05, 8.85367081e-06, 9.64845640e-06, 2.54260249e-05, 3.50545884e-06, nan, nan, nan, nan, nan], dtype=float32) - Kd_heat(zeuc)float321.001e-06 1.001e-06 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([1.0006723e-06, 1.0006611e-06, 1.0006628e-06, 1.0006404e-06, 1.0006654e-06, 1.0006577e-06, 1.0006396e-06, 1.0006671e-06, 1.0006659e-06, 1.0006629e-06, 1.0006570e-06, 1.0006389e-06, 1.0006675e-06, 1.0019558e-06, 1.0135477e-06, 1.0423036e-06, 1.2229641e-06, 1.4524945e-06, 1.7310816e-05, 1.5045007e-05, 3.8584258e-06, 6.2752624e-06, 8.8590523e-06, 1.3132055e-05, 1.0210648e-05, 2.2401643e-06, 3.0851486e-06, 1.0444164e-06, 1.0006403e-06, 6.2561652e-05, 1.0642791e-06, 7.5733333e-05, 4.5665741e-04, 7.8447431e-04, 1.5393369e-03, 3.8610261e-03, 8.3111003e-03, 1.1603486e-02, 1.4628670e-02, 1.4060708e-02, 1.3308118e-02, 1.1162918e-02, 1.2060479e-02, 2.7877459e-02, 5.9886063e-03, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float326.88e-10 7.366e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([6.8802869e-10, 7.3664508e-10, 8.5824386e-10, 9.8956543e-10, 1.0133299e-09, 1.2501800e-09, 1.3284884e-09, 1.4034628e-09, 1.5236361e-09, 1.4596947e-09, 1.4793824e-09, 1.2308681e-09, 1.2104571e-09, 9.6535835e-10, 8.2865687e-10, 5.9357264e-10, 5.8739025e-10, 8.6316454e-10, 1.1760135e-09, 2.8925231e-09, 4.5060946e-09, 1.1985256e-08, 2.2661380e-08, 4.0463782e-08, 3.4963129e-08, 1.8348556e-08, 2.2507315e-08, 2.5304370e-08, 4.1292687e-08, 2.5772588e-08, 2.9690797e-08, 1.0108992e-06, 2.0222787e-06, 1.5631721e-06, 1.1606248e-06, 8.0264130e-07, 5.3737216e-07, 4.3478525e-07, 4.4919923e-07, 5.4489811e-07, 4.6658363e-07, 4.9533986e-07, 3.4559400e-07, 3.4655343e-07, 4.7845720e-07, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float32-3.694e-11 -3.776e-11 ... nan nan
- long_name :
- $SP$
- units :
- W/kg
array([-3.6943056e-11, -3.7760486e-11, -4.2039410e-11, -4.5992953e-11, -4.6207090e-11, -5.3252826e-11, -5.4613803e-11, -5.6314536e-11, -5.9378800e-11, -5.5845037e-11, -5.6469246e-11, -5.1201848e-11, -4.9829935e-11, -4.3215653e-11, -3.6598721e-11, -2.7171746e-11, -4.9472245e-12, 1.2602305e-10, 1.2094480e-10, 2.1497398e-10, 5.6442151e-10, 1.2130693e-09, 1.1651345e-09, 1.2494932e-09, 1.8302352e-09, 1.1315205e-09, 1.1661196e-09, 7.3920425e-10, 2.0309896e-09, -6.3493918e-11, 2.3585470e-08, 2.6165077e-07, 1.7611094e-07, 2.9128773e-07, 3.8452137e-07, 4.2090971e-07, 3.9566024e-07, 3.6361661e-07, 3.7896899e-07, 4.6042950e-07, 4.7241875e-07, 4.9391775e-07, 6.4561976e-07, 9.3485789e-07, 2.2282279e-06, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float323.245e-07 5.718e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([3.2451570e-07, 5.7184326e-07, 4.3148790e-07, 6.0657896e-07, 6.7605208e-07, 5.9352175e-07, 8.2924151e-07, 8.5900632e-07, 8.6205756e-07, 1.0500863e-06, 8.3261909e-07, 7.6358799e-07, 7.8491541e-07, 7.2332011e-07, 1.5663835e-06, 2.1174208e-06, 7.1297836e-06, 1.3430081e-05, 2.4580020e-05, 4.0981133e-05, 6.2299107e-05, 9.6839991e-05, 1.3706491e-04, 1.7594828e-04, 1.9717270e-04, 1.9812472e-04, 1.8732122e-04, 1.7612877e-04, 1.7498141e-04, 4.2245716e-05, 1.6423324e-04, 1.1454133e-03, 5.2541826e-04, 4.1192962e-04, 3.3999418e-04, 2.7700741e-04, 2.1282340e-04, 1.6937664e-04, 1.5941910e-04, 1.7767286e-04, 1.6364509e-04, 1.8765387e-04, 1.9949353e-04, 1.1106770e-04, 1.6636330e-04, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32186.3 128.5 132.1 ... nan nan nan
- long_name :
- $Ri^g_T$
array([1.8634216e+02, 1.2853084e+02, 1.3210474e+02, 1.1979617e+02, 1.0202711e+02, 1.0279958e+02, 9.7481445e+01, 8.4427910e+01, 9.3046837e+01, 8.0721817e+01, 1.0160206e+02, 9.5520500e+01, 9.3262207e+01, 1.0652971e+02, 5.7025261e+01, 4.3027679e+01, 1.1232176e+01, 4.4198289e+00, 1.9743326e+00, 1.4598441e+00, 1.1865945e+00, 9.0280277e-01, 7.5970727e-01, 6.7375970e-01, 6.7109162e-01, 8.1848937e-01, 1.0103055e+00, 1.4284228e+00, 2.1402125e+00, 1.0676737e+01, 7.4439979e+00, 3.3229971e-01, 3.1535965e-01, 2.4582326e-01, 1.9892570e-01, 1.7789739e-01, 1.8457843e-01, 2.1493007e-01, 2.4419188e-01, 1.9702415e-01, 1.6759664e-01, 1.4394848e-01, 1.8335819e-01, 3.7373954e-01, 5.9188493e-02, nan, nan, nan, nan, nan], dtype=float32) - uo(zeuc)float32-0.01065 0.02048 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-1.0646891e-02, 2.0477358e-02, 1.1050474e-02, -8.4186951e-03, 1.7257325e-02, 1.1625963e-02, -6.5456191e-03, 1.3428366e-02, -4.1635828e-03, 8.3168782e-03, -3.1610057e-04, -4.4213003e-03, 4.7586383e-03, -1.6103460e-03, 5.9380159e-03, 1.0472262e-02, 2.0668298e-02, 4.8041590e-02, 7.7255875e-02, 1.3745204e-01, 1.9449836e-01, 2.8766719e-01, 4.3414849e-01, 5.1080972e-01, 6.4297825e-01, 7.6984107e-01, 9.1899163e-01, 1.0664825e+00, 1.1405255e+00, 1.2641385e+00, 1.2902447e+00, 1.2021922e+00, 9.1631228e-01, 6.8355072e-01, 5.1118356e-01, 3.5848483e-01, 2.2015458e-01, 1.0378160e-01, 1.9374512e-02, -3.7018765e-02, -9.4496332e-02, -4.8449118e-02, -4.7313720e-02, 1.0204794e-01, 5.5657301e-02, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float32-0.0004901 0.0005714 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-0.00049009, 0.00057143, -0.00114546, -0.00014106, 0.00080116, -0.00064699, -0.00018529, 0.00069077, 0.00067059, -0.0008774 , 0.00098835, -0.00173778, -0.00020317, -0.00044024, 0.00021668, -0.0002896 , -0.00128701, -0.00099582, -0.00125251, -0.00213526, -0.00148937, -0.0002665 , -0.00644217, -0.0011105 , -0.00474673, -0.00347817, -0.0042568 , -0.0022129 , -0.00164185, -0.00106187, -0.00665514, -0.0052943 , -0.00831086, -0.01532346, -0.02331558, -0.02841561, -0.02930233, -0.0267921 , -0.02194007, -0.01483669, -0.00246736, -0.00684613, -0.0120682 , 0.09584202, 0.11942156, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float325.046e-06 5.043e-06 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([5.0464682e-06, 5.0427479e-06, 5.0452982e-06, 5.0455860e-06, 5.0420449e-06, 5.0442563e-06, 5.0445096e-06, 5.0414201e-06, 5.0418244e-06, 5.0409303e-06, 5.0384342e-06, 5.0429262e-06, 5.0398326e-06, 5.0403924e-06, 5.0384015e-06, 5.0397739e-06, 5.0431672e-06, 5.1221195e-06, 1.2207116e-05, 1.7747578e-05, 1.4220629e-05, 1.1861085e-05, 1.7739267e-05, 1.5002135e-05, 1.2204411e-05, 1.0666293e-05, 6.1089358e-06, 5.0663079e-06, 5.0405142e-06, 2.5051277e-05, 2.0913076e-05, 5.0343264e-05, 3.2911345e-04, 7.6059770e-04, 1.3257149e-03, 2.7636024e-03, 5.2530593e-03, 7.9990737e-03, 9.9148713e-03, 1.0457821e-02, 8.8949250e-03, 9.7109433e-03, 9.9065546e-03, 1.7521054e-02, 3.3595981e-03, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float3210.29 10.42 10.62 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([10.287295 , 10.417949 , 10.615116 , 10.869449 , 11.04266 , 11.262654 , 11.567776 , 11.807474 , 12.077805 , 12.501555 , 12.587448 , 12.865992 , 13.096592 , 13.335708 , 13.5657015, 13.746414 , 13.889223 , 14.036531 , 14.221035 , 14.368961 , 14.648155 , 15.087256 , 15.539373 , 15.975408 , 16.745865 , 17.152761 , 18.009958 , 19.28983 , 20.677986 , 21.19363 , 22.335844 , 23.474924 , 24.749414 , 25.383026 , 25.68066 , 25.866386 , 26.00263 , 26.103117 , 26.162252 , 26.248213 , 26.393143 , 26.920116 , 27.855074 , 26.945793 , 26.995775 , nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: yh float64 0.0625 yq float64 -0.0625 xh float64 -140.0 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: Tflx_dia_diff (zeuc) float32 1.85e-08 1.913e-08 2.062e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 6.88e-10 7.366e-10 8.582e-10 ... nan nan nan eps (zeuc) float32 -3.694e-11 -3.776e-11 -4.204e-11 ... nan nan S2 (zeuc) float32 3.245e-07 5.718e-07 4.315e-07 ... nan nan nan Rig_T (zeuc) float32 186.3 128.5 132.1 119.8 ... nan nan nan nan uo (zeuc) float32 -0.01065 0.02048 0.01105 ... nan nan nan vo (zeuc) float32 -0.0004901 0.0005714 -0.001145 ... nan nan nan ν (zeuc) float32 5.046e-06 5.043e-06 5.045e-06 ... nan nan nan thetao (zeuc) float32 10.29 10.42 10.62 10.87 ... nan nan nan nannew_baseline.kpp.lmd.004- zeuc: 50
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- Tflx_dia_diff(zeuc)float321.845e-08 1.901e-08 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
array([1.84522388e-08, 1.90133331e-08, 1.98160350e-08, 2.17871534e-08, 2.19269296e-08, 2.31987691e-08, 2.58633701e-08, 2.56593573e-08, 2.66053508e-08, 2.70146554e-08, 2.64776752e-08, 2.54793733e-08, 2.51310990e-08, 2.30840840e-08, 2.02013766e-08, 1.97913277e-08, 2.01755146e-08, 2.69346945e-08, 2.62779469e-08, 5.28789563e-08, 5.48875114e-08, 1.00109666e-07, 2.71216010e-07, 3.19583648e-07, 3.74500303e-07, 1.46047938e-07, 1.16343891e-07, 1.03832775e-07, 1.41212709e-07, 1.06493850e-07, 1.35873535e-07, 6.90023035e-06, 1.85962454e-05, 2.29003454e-05, 2.42327587e-05, 2.49604745e-05, 2.66399857e-05, 2.77722211e-05, 2.66196348e-05, 2.17321231e-05, 1.58120238e-05, 9.36440847e-06, 1.10887204e-05, 2.19333415e-05, 1.07785513e-06, nan, nan, nan, nan, nan], dtype=float32) - Kd_heat(zeuc)float321.001e-06 1.001e-06 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([1.00067518e-06, 1.00065427e-06, 1.00066927e-06, 1.00063642e-06, 1.00066666e-06, 1.00066268e-06, 1.00063505e-06, 1.00066575e-06, 1.00066745e-06, 1.00066438e-06, 1.00066154e-06, 1.00063460e-06, 1.00101636e-06, 1.00151851e-06, 1.00441002e-06, 1.06810353e-06, 1.41212877e-06, 1.91577578e-06, 1.80881364e-06, 1.60107575e-05, 2.37403265e-06, 4.09625409e-06, 9.22853906e-06, 2.46128784e-05, 1.08849845e-05, 4.00248064e-06, 2.07656421e-06, 1.03766467e-06, 1.00063471e-06, 1.10138728e-06, 1.06313692e-05, 8.53215824e-05, 4.45734942e-04, 9.08712565e-04, 1.99472369e-03, 4.84049134e-03, 1.07121104e-02, 1.64499823e-02, 2.01246608e-02, 1.98929477e-02, 1.70502812e-02, 1.29411258e-02, 1.47804301e-02, 3.19275931e-02, 6.53247582e-03, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float326.837e-10 7.26e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([6.8373696e-10, 7.2600542e-10, 7.9185863e-10, 9.5713926e-10, 9.6860342e-10, 1.0881023e-09, 1.3508273e-09, 1.3291162e-09, 1.4311416e-09, 1.4834852e-09, 1.4281516e-09, 1.3238612e-09, 1.3013337e-09, 1.1062754e-09, 8.5938778e-10, 8.0222251e-10, 6.7409117e-10, 8.7976160e-10, 9.4508401e-10, 2.2733280e-09, 3.1751306e-09, 6.8161152e-09, 2.0803506e-08, 2.8093355e-08, 3.7279484e-08, 1.6553953e-08, 1.9269990e-08, 2.3974705e-08, 4.3170928e-08, 2.6435435e-08, 3.5509224e-08, 1.4844380e-06, 2.2649865e-06, 1.8430437e-06, 1.2526549e-06, 7.6951369e-07, 4.7593264e-07, 3.4695140e-07, 3.5711125e-07, 4.3343695e-07, 4.5873477e-07, 4.6846111e-07, 4.6900448e-07, 2.3183331e-07, 4.6221166e-07, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float32-3.68e-11 -3.704e-11 ... nan nan
- long_name :
- $SP$
- units :
- W/kg
array([-3.6798245e-11, -3.7043757e-11, -3.9815873e-11, -4.5821649e-11, -4.5212632e-11, -4.8938409e-11, -5.6257804e-11, -5.5064016e-11, -5.7241569e-11, -5.7495130e-11, -5.5803029e-11, -5.4849288e-11, -5.2586990e-11, -4.7781890e-11, -3.8841354e-11, -1.7202939e-11, -2.8121089e-11, -2.9045366e-12, 1.4461059e-10, 9.7483209e-11, 2.7008534e-10, 9.2158514e-10, 1.2863184e-09, 8.2257207e-10, 2.1664082e-09, 1.3764505e-09, 9.4874741e-10, 8.4966906e-10, 7.3667694e-10, 2.4134991e-10, 2.3388990e-08, 3.1551630e-07, 2.2120491e-07, 3.3275964e-07, 3.8239853e-07, 3.7358600e-07, 3.0824830e-07, 2.2744673e-07, 2.2537803e-07, 2.6028704e-07, 2.9770609e-07, 3.6009791e-07, 3.5931723e-07, 1.0959816e-07, 4.3092624e-07, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float322.544e-07 6.384e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([2.54378733e-07, 6.38353811e-07, 4.58427763e-07, 3.80050096e-07, 5.64058951e-07, 6.36048640e-07, 5.56854729e-07, 7.26399946e-07, 9.05369006e-07, 9.45442423e-07, 8.51951029e-07, 6.91803280e-07, 7.55714382e-07, 7.04647334e-07, 1.26243197e-06, 2.71247109e-06, 4.65858102e-06, 1.16520087e-05, 1.99510814e-05, 3.31415249e-05, 5.00230417e-05, 8.05205273e-05, 1.27244726e-04, 1.69002291e-04, 2.03346484e-04, 2.10992672e-04, 1.87548270e-04, 1.89796614e-04, 2.03424614e-04, 5.08211524e-05, 1.83125841e-04, 1.33370166e-03, 6.43725973e-04, 4.54566209e-04, 3.34972632e-04, 2.33477040e-04, 1.48811640e-04, 1.01950354e-04, 9.15324563e-05, 9.83889113e-05, 1.07818625e-04, 1.17441712e-04, 1.18318312e-04, 4.21129625e-05, 1.39109572e-04, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32226.6 131.1 150.5 ... nan nan nan
- long_name :
- $Ri^g_T$
array([ 2.26648071e+02, 1.31057510e+02, 1.50471497e+02, 1.48722946e+02, 1.45639999e+02, 1.06133774e+02, 1.36979568e+02, 1.12499794e+02, 8.02135468e+01, 8.84140244e+01, 8.54823303e+01, 1.19611740e+02, 9.99403992e+01, 1.19359146e+02, 7.77576904e+01, 4.18875122e+01, 1.76356297e+01, 4.95762348e+00, 2.53250837e+00, 1.47906017e+00, 1.34763014e+00, 9.56021309e-01, 7.78292775e-01, 6.81925893e-01, 6.09881163e-01, 6.72925949e-01, 9.91986275e-01, 1.26318133e+00, 1.82970357e+00, 8.63165855e+00, 6.78957748e+00, 3.14613789e-01, 3.16555321e-01, 2.50890136e-01, 2.09905684e-01, 2.02570021e-01, 2.29070097e-01, 2.94496417e-01, 3.63696158e-01, 3.25371534e-01, 2.26018295e-01, 1.37651816e-01, 2.03731701e-01, 3.91990632e-01, -2.14977749e-03, nan, nan, nan, nan, nan], dtype=float32) - uo(zeuc)float32-0.005893 0.005001 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-5.8929874e-03, 5.0010150e-03, 2.4871632e-02, -1.3677386e-05, 4.5415116e-03, 2.2741454e-02, 6.3635116e-03, 2.4531148e-03, 1.5222704e-02, -1.3702812e-03, 1.2736225e-02, 1.6036917e-02, -4.4084731e-03, 1.6050644e-02, -1.1332115e-03, 2.5234869e-02, 1.7870275e-02, 5.3290766e-02, 7.1112156e-02, 1.2818980e-01, 1.8416606e-01, 2.5570861e-01, 4.1997769e-01, 4.7003269e-01, 5.9571236e-01, 7.3374313e-01, 8.8366783e-01, 1.0486763e+00, 1.1454952e+00, 1.2573735e+00, 1.2901310e+00, 1.1977949e+00, 8.7613827e-01, 6.3310784e-01, 4.5654660e-01, 3.0633986e-01, 1.8709742e-01, 9.5162839e-02, 2.5411908e-02, -2.0137414e-02, -6.1557423e-02, -4.7269031e-02, 8.9613520e-02, 2.3487601e-01, 1.9478835e-01, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float32-0.002198 0.0004198 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-2.19777389e-03, 4.19814343e-04, 7.79381837e-04, -1.63761433e-03, 5.18743531e-04, 1.12472579e-03, -1.14625844e-03, 9.12419346e-04, 4.86612611e-04, 1.00931247e-04, 1.75473702e-04, -1.71095878e-03, 7.74093845e-04, -5.65544877e-04, 1.16517756e-03, -3.99415003e-04, -1.95945366e-04, -9.65662650e-04, -1.62107041e-04, -1.39739586e-03, 1.30683271e-04, -2.51332135e-03, -5.16847987e-03, -3.49007547e-03, -4.20303456e-03, -5.73705230e-03, -7.36995926e-03, -6.09641057e-03, 1.46760372e-04, -2.56395293e-03, -8.92745517e-03, -6.60287915e-03, -5.58094215e-03, -1.28454212e-02, -2.20838133e-02, -2.92490181e-02, -3.26998085e-02, -3.21489237e-02, -2.89497189e-02, -2.41536312e-02, -1.76583268e-02, -1.93542968e-02, -3.83493304e-02, -1.23079315e-01, -1.05593860e-01, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float325.049e-06 5.043e-06 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([5.0486437e-06, 5.0429589e-06, 5.0442504e-06, 5.0475896e-06, 5.0422968e-06, 5.0433819e-06, 5.0462104e-06, 5.0418989e-06, 5.0425606e-06, 5.0407480e-06, 5.0382882e-06, 5.0448925e-06, 5.0402373e-06, 5.0409162e-06, 5.0385738e-06, 5.0399153e-06, 7.4432128e-06, 5.0412550e-06, 7.5570620e-06, 1.4555165e-05, 1.3303173e-05, 9.6282793e-06, 1.8854163e-05, 1.3644422e-05, 1.4976369e-05, 1.1149726e-05, 6.3058492e-06, 5.0542981e-06, 5.0390413e-06, 6.6572366e-06, 9.9026156e-06, 4.6662979e-05, 3.2871615e-04, 8.2944619e-04, 1.6036916e-03, 3.3080974e-03, 6.4689615e-03, 1.0309312e-02, 1.3183054e-02, 1.3386927e-02, 1.0917425e-02, 1.0970640e-02, 1.1653668e-02, 1.6986607e-02, 3.7777168e-03, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float3210.23 10.36 10.51 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([10.228951 , 10.357553 , 10.5078 , 10.813727 , 10.959333 , 11.150814 , 11.475641 , 11.667459 , 11.963285 , 12.336132 , 12.506242 , 12.775228 , 12.946938 , 13.233289 , 13.456113 , 13.663487 , 13.828789 , 13.956572 , 14.125122 , 14.294209 , 14.496647 , 14.8735285, 15.366182 , 15.701226 , 16.427809 , 16.800098 , 17.615082 , 18.836851 , 20.428156 , 20.803474 , 21.94135 , 23.154457 , 24.625856 , 25.270407 , 25.569725 , 25.756596 , 25.884304 , 25.971855 , 26.026701 , 26.067236 , 26.111523 , 26.287266 , 26.828798 , 27.264927 , 27.29486 , nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: yh float64 0.0625 yq float64 -0.0625 xh float64 -140.0 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: Tflx_dia_diff (zeuc) float32 1.845e-08 1.901e-08 1.982e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 6.837e-10 7.26e-10 7.919e-10 ... nan nan nan eps (zeuc) float32 -3.68e-11 -3.704e-11 -3.982e-11 ... nan nan S2 (zeuc) float32 2.544e-07 6.384e-07 4.584e-07 ... nan nan nan Rig_T (zeuc) float32 226.6 131.1 150.5 148.7 ... nan nan nan nan uo (zeuc) float32 -0.005893 0.005001 0.02487 ... nan nan nan vo (zeuc) float32 -0.002198 0.0004198 0.0007794 ... nan nan nan ν (zeuc) float32 5.049e-06 5.043e-06 5.044e-06 ... nan nan nan thetao (zeuc) float32 10.23 10.36 10.51 10.81 ... nan nan nan nannew_baseline.kpp.lmd.005
euc_mean.to_netcdf("mom6-euc-mean.nc")
for nodename, node in micro_zeuc.children.items():
euc_mean[nodename] = node
h = {
varname: mixpods.map_hvplot(
lambda node, name, muted: (
node.ds.cf[varname]
.reset_coords(drop=True)
.hvplot.line(
ylabel=varname,
label=name,
logx=varname != "sea_water_x_velocity",
invert=True,
)
),
euc_mean,
)
for varname in [
"sea_water_x_velocity",
"sea_water_potential_temperature",
"chi",
"eps",
"ocean_vertical_heat_diffusivity",
"ocean_vertical_momentum_diffusivity",
]
}
h["chi"].opts(ylim=(1e-9, 1e-4))
h["eps"].opts(ylim=(1e-9, 1e-4))
h["ocean_vertical_heat_diffusivity"].opts(ylim=(5e-7, 3))
h["ocean_vertical_momentum_diffusivity"].opts(ylim=(1e-6, 1e-1))
h2 = {
varname: mixpods.map_hvplot(
lambda node, name, muted: node.ds[varname]
.reset_coords(drop=True)
.hvplot.line(ylabel=varname, label=name, logx=False, invert=True),
euc_mean,
)
for varname in ["Rig_T"]
}
hv.Layout(list(h.values()) + [h2["Rig_T"].opts(ylim=(0, 3))]).opts(
hv.opts.Curve(frame_width=150, frame_height=300, xlim=(-150, 150)),
hv.opts.Layout(shared_axes=True),
hv.opts.Overlay(show_legend=True, show_grid=True, legend_position="right"),
*mixpods.HV_TOOLS_OPTIONS,
*mixpods.PRESENTATION_OPTS,
).cols(4)
Ri = (
tree.dc.extract_leaf("euc")
.dc.subset_nodes(["Rig_T"])
.sel(time=slice("1992", "2017"))
.load()
)
mixpods.plot_distributions(
Ri.dc.reorder_nodes(["TAO", ...]),
"Rig_T",
bins=np.arange(0, 5, 0.25),
log=False,
subset=False,
).opts(hv.opts.Overlay(legend_position="right"))
# tree["new_baseline.hb/euc"]["Rig_T"].sel(zeuc=slice(-120, -30)).hvplot.hist(bins=[0, 0.2, 0.5, 0.8, 1, 2, 3, 4, 5])
# tree["baseline/euc"]["Rig_T"].sel(zeuc=slice(-120, -30)).hvplot.hist(bins=[0, 0.2, 0.5, 0.8, 1, 2, 3, 4, 5])
catalog.search(frequency="monthly").df
| casename | stream | path | baseline | levels | frequency | variables | shortname | description | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | h | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [N2_int, agessc, h, rhopot0, so, thetao, uhGM,... | baseline.001 | baseline |
| 1 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | hm | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [Heat_PmE, KE, Rd_dx, SSH, SSU, SSV, T_adx_2d,... | baseline.001 | baseline |
| 2 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | sfc | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [Rd_dx, SSH, SSU, SSV, mass_wt, mlotst, oml, o... | baseline.001 | baseline |
| 3 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | h | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [h, so, thetao, uhGM, uhml, umo, uo, vhGM, vhm... | baseline.epbl.001 | ePBL |
| 4 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | sfc | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [SSH, SSU, SSV, mlotst, sos, speed, tos] | baseline.epbl.001 | ePBL |
| 5 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | wci | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [T_advection_xy, T_lbdxy_cont_tendency, Th_ten... | baseline.epbl.001 | ePBL |
| 6 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | h | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [h, so, thetao, uhGM, uhml, umo, uo, vhGM, vhm... | baseline.kpp.lmd.002 | KPP Ri0=0.5 |
| 7 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | sfc | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [SSH, SSU, SSV, mlotst, oml, sos, speed, tos] | baseline.kpp.lmd.002 | KPP Ri0=0.5 |
| 8 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | wci | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [KPP_NLT_temp_budget, T_advection_xy, T_lbdxy_... | baseline.kpp.lmd.002 | KPP Ri0=0.5 |
| 9 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | h | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [h, so, thetao, uhGM, uhml, umo, uo, vhGM, vhm... | baseline.kpp.lmd.003 | KPP Ri0=0.5, Ric=0.2, |
| 10 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | sfc | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [SSH, SSU, SSV, mlotst, oml, sos, speed, tos] | baseline.kpp.lmd.003 | KPP Ri0=0.5, Ric=0.2, |
| 11 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | wci | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [KPP_NLT_temp_budget, T_advection_xy, T_lbdxy_... | baseline.kpp.lmd.003 | KPP Ri0=0.5, Ric=0.2, |
| 12 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | h | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [h, so, thetao, uhGM, uhml, umo, uo, vhGM, vhm... | baseline.kpp.lmd.004 | KPP ν0=2.5, Ric=0.2, Ri0=0.5 |
| 13 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | sfc | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [SSH, SSU, SSV, mlotst, oml, sos, speed, tos] | baseline.kpp.lmd.004 | KPP ν0=2.5, Ric=0.2, Ri0=0.5 |
| 14 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.... | wci | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | old | 65 | monthly | [KPP_NLT_temp_budget, T_advection_xy, T_lbdxy_... | baseline.kpp.lmd.004 | KPP ν0=2.5, Ric=0.2, Ri0=0.5 |
| 15 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_basel... | h | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | new | 65 | monthly | [N2_int, agessc, h, rhopot0, so, thetao, uhGM,... | new_baseline.hb | KD=0, KV=0 |
| 16 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_basel... | hm | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | new | 65 | monthly | [Heat_PmE, KE, KPP_NLT_temp_budget, Rd_dx, SSH... | new_baseline.hb | KD=0, KV=0 |
| 17 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_basel... | h | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | new | 65 | monthly | [N2_int, agessc, h, rhopot0, so, thetao, uhGM,... | new_baseline.kpp.lmd.004 | KPP ν0=2.5, Ric=0.2, Ri0=0.5 |
| 18 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_basel... | hm | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | new | 65 | monthly | [Heat_PmE, KE, KPP_NLT_temp_budget, Rd_dx, SSH... | new_baseline.kpp.lmd.004 | KPP ν0=2.5, Ric=0.2, Ri0=0.5 |
| 19 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_basel... | h | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | new | 65 | monthly | [N2_int, agessc, h, rhopot0, so, thetao, uhGM,... | new_baseline.kpp.lmd.005 | KPP ν0=2.5, Ri0=0.5 |
| 20 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_basel... | hm | /glade/campaign/cgd/oce/projects/pump/cesm/gmo... | new | 65 | monthly | [Heat_PmE, KE, KPP_NLT_temp_budget, Rd_dx, SSH... | new_baseline.kpp.lmd.005 | KPP ν0=2.5, Ri0=0.5 |
monthly = catalog.search(frequency="monthly", variables="uo").to_datatree()
--> The keys in the returned dictionary of datasets are constructed as follows:
'shortname/stream'
uo = (
monthly.drop_nodes(
set(monthly)
- set(
tree.dc.rename_nodes(
{"baseline": "baseline.001", "kpp.lmd.004": "baseline.kpp.lmd.004"}
)
)
)
.dc.extract_leaf("h")
.sel(xq=-140, method="nearest")
.sel(yh=slice(-10, 10))
.sel(time=slice("0046-01-01", "0058-01-01"))
# .mean("time")
).load()
(
hv.Layout(
[
node["uo"]
.where(node["uo"] < 10)
.reset_coords(drop=True)
.hvplot.quadmesh(x="yh", clim=(-0.01, 0.01), cmap="coolwarm", title=name)
for name, node in uo.mean("time").children.items()
]
)
.cols(2)
.opts(
hv.opts.QuadMesh(invert_yaxis=True, ylim=(2000, 0)),
*mixpods.PRESENTATION_OPTS,
*mixpods.HV_TOOLS_OPTIONS,
)
)
Test out available variables#
KPP_Kheatis non-zero only in KPP_OBL
(mom6140.Tflx_dia_diff * 1025 * 4000).cf.plot(robust=True)
<matplotlib.collections.QuadMesh>
mom6140.Kv_u.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))
<matplotlib.collections.QuadMesh>
mom6140.Kd_heat.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))
mom6140.KPP_OBLdepth.plot(color="r")
plt.figure()
mom6140.KPP_kheat.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))
mom6140.KPP_OBLdepth.plot(color="r")
plt.figure()
(mom6140.KPP_kheat - mom6140.Kd_heat).cf.plot(
robust=True, cmap=mpl.cm.mpl.cm.Reds_r, vmax=0
)
<matplotlib.collections.QuadMesh>
Experiment with manual combining#
from intake.source.utils import reverse_format
years = []
tiles = []
for file in files[:10]:
p = pathlib.Path(file)
params = reverse_format("__{year}.nc.{tile}", p.stem + p.suffix)
years.append(params["year"])
tiles.append(params["tile"])
years, tiles
import toolz as tlz
bytile = {}
for tile, v in tlz.groupby(tileid, files).items():
bytile[tile] = xr.concat(read_raw_files(v, parallel=True), dim="time")
print("\n".join([hash(ds.yh.data.tolist()) for ds in list(bytile.values())]))
from functools import partial
def hash_coords(ds, axis):
dims = ds.cf.axes[axis]
data = np.concatenate([ds[dim].data for dim in dims])
return hash(tuple(data))
grouped = bytile
for axis, concat_axis in [("X", "Y"), ("Y", "X")]:
grouped = tlz.groupby(partial(hash_coords, axis=axis), grouped.values())
grouped = {
k: cfconcat(v, axis=concat_axis, join="exact") for k, v in grouped.items()
}
break
grouped
cfconcat(list(grouped.values()), "X")
combined = xr.combine_by_coords(list(bytile.values()), combine_attrs="override")
def raise_if_bad_index(combined):
bad = []
for idx in combined.indexes:
index = combined.indexes[idx]
if not index.is_monotonic or index.has_duplicates:
bad.append(idx)
if bad:
raise ValueError(f"Indexes {idx} are either not monotonic or have duplicates")
def tileid(path):
p = pathlib.Path(path)
# print(p.suffix)
# params = reverse_format("__{year}.nc.{tile}", p.stem + p.suffix)
return int(p.suffix[1:]) # params["tile"]
# years.append(params["year"])
# tiles.append(params["tile"])
sorted_files = sorted(files, key=tileid)
for tile, files in groupby(sorted_files, tileid):
print(tile, len(list(files)))
Test out ONI#
Phase labeling#
oni = pump.obs.process_oni().sel(time=slice("2005-Sep", None))
enso_transition = mixpods.make_enso_transition_mask(oni)
mixpods.plot_enso_transition(oni, enso_transition)
Replicate ONI calculation#
close enough!
ersst = xr.tutorial.open_dataset("ersstv5")
monthlyersst = (
ersst.sst.cf.sortby("Y")
.cf.sel(latitude=slice(-5, 5), longitude=slice(360 - 170, 360 - 120))
.cf.mean(["X", "Y"])
.resample(time="M")
.mean()
.load()
)
expected = mixpods.calc_oni(monthlyersst)
actual = pump.obs.process_oni()
actual.plot()
expected.plot()
(actual - expected).plot()
[<matplotlib.lines.Line2D>]
Comparing to Warner and Moum code#
chipod.eps.sel(time="2007-01-14 10:35:00", method="nearest").load()
<xarray.DataArray 'eps' (depth: 5)>
nan 3.929e-07 2.846e-07 2.234e-07 4.491e-07
Coordinates:
time datetime64[ns] 2007-01-14T11:00:00
* depth (depth) float64 -69.0 -59.0 -49.0 -39.0 -29.0
timeSeries (depth) float64 69.0 59.0 49.0 39.0 29.0
lat (depth) float64 0.0 0.0 0.0 0.0 0.0
lon (depth) float64 -140.0 -140.0 -140.0 -140.0 -140.0
Attributes:
long_name: turbulence dissipation rate
standard_name: specific_turbulent_kinetic_energy_dissipation_in_...
ncei_name: turbulent kinetic energy (TKE) dissipation rate
units: W kg-1
FillValue: -9999
valid_min: 1e-12
valid_max: 9.999999999999999e-06
coverage_content_type: physicalMeasurement
grid_mapping: crs
source: inferred from fast thermistor spectral scaling
references: Moum J.N. and J.D. Nash, Mixing measurements on a...
cell_methods: depth: point, time: mean
platform: mooring
instrument: chipod- depth: 5
- nan 3.929e-07 2.846e-07 2.234e-07 4.491e-07
array([ nan, 3.92899514e-07, 2.84615955e-07, 2.23436769e-07, 4.49053774e-07]) - time()datetime64[ns]2007-01-14T11:00:00
- long_name :
- Time, UTC
- standard_name :
- time
- axis :
- T
array('2007-01-14T11:00:00.000000000', dtype='datetime64[ns]') - depth(depth)float64-69.0 -59.0 -49.0 -39.0 -29.0
- long_name :
- depths of sensors along the mooring line
- standard_name :
- depth
- units :
- m
- axis :
- Z
- positive :
- up
- valid_min :
- 29.0
- valid_max :
- 119.0
array([-69., -59., -49., -39., -29.])
- timeSeries(depth)float6469.0 59.0 49.0 39.0 29.0
- long_name :
- depths of Time series of T, dTdz, Nsqr, chi, epsilon, and Jq along a mooring line at 0-140W
- cf_role :
- timeseries_id
array([69., 59., 49., 39., 29.])
- lat(depth)float640.0 0.0 0.0 0.0 0.0
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
- axis :
- Y
- valid_min :
- 0.0
- valid_max :
- 0.0
array([0., 0., 0., 0., 0.])
- lon(depth)float64-140.0 -140.0 -140.0 -140.0 -140.0
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
- axis :
- X
- valid_min :
- -140.0
- valid_max :
- -140.0
array([-140., -140., -140., -140., -140.])
- long_name :
- turbulence dissipation rate
- standard_name :
- specific_turbulent_kinetic_energy_dissipation_in_sea_water
- ncei_name :
- turbulent kinetic energy (TKE) dissipation rate
- units :
- W kg-1
- FillValue :
- -9999
- valid_min :
- 1e-12
- valid_max :
- 9.999999999999999e-06
- coverage_content_type :
- physicalMeasurement
- grid_mapping :
- crs
- source :
- inferred from fast thermistor spectral scaling
- references :
- Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf
- cell_methods :
- depth: point, time: mean
- platform :
- mooring
- instrument :
- chipod
tao_gridded.enso_transition.loc[{"time": slice("2015-12", "2016-01")}]
<xarray.DataArray 'enso_transition' (time: 1488)>
'El-Nino warm' 'El-Nino warm' 'El-Nino warm' ... 'El-Nino warm' 'El-Nino warm'
Coordinates: (12/13)
deepest (time) float64 -300.0 -300.0 -300.0 ... -300.0 -300.0
eucmax (time) float64 -135.0 -140.0 -135.0 ... -140.0 -135.0
latitude float32 0.0
longitude float32 -140.0
mld (time) float64 -9.0 -8.0 -8.0 -8.0 ... nan nan nan nan
mldT (time) float64 -10.0 -8.0 -8.0 -8.0 ... nan nan nan nan
... ...
shallowest (time) float64 -5.0 -5.0 -5.0 -5.0 ... -10.0 -10.0 -10.0
* time (time) datetime64[ns] 2015-12-01 ... 2016-01-31T23:00:00
oni (time) float32 2.53 2.53 2.53 2.53 ... 2.53 2.53 2.53
warm_mask (time) bool True True True True ... False False False
cool_mask (time) bool False False False False ... True True True
enso_transition (time) <U12 'El-Nino warm' ... 'El-Nino warm'
Attributes:
description: Warner & Moum (2019) ENSO transition phase; El-Nino = ONI >...- time: 1488
- 'El-Nino warm' 'El-Nino warm' ... 'El-Nino warm' 'El-Nino warm'
array(['El-Nino warm', 'El-Nino warm', 'El-Nino warm', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12') - deepest(time)float64-300.0 -300.0 ... -300.0 -300.0
- description :
- Deepest depth with a valid observation
- units :
- m
array([-300., -300., -300., ..., -300., -300., -300.])
- eucmax(time)float64-135.0 -140.0 ... -140.0 -135.0
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
array([-135., -140., -135., ..., -140., -140., -135.])
- latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- mld(time)float64-9.0 -8.0 -8.0 -8.0 ... nan nan nan
- long_name :
- $z_{MLD}$
- units :
- m
array([-9., -8., -8., ..., nan, nan, nan])
- mldT(time)float64-10.0 -8.0 -8.0 ... nan nan nan
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
array([-10., -8., -8., ..., nan, nan, nan])
- reference_pressure()int640
array(0)
- shallowest(time)float64-5.0 -5.0 -5.0 ... -10.0 -10.0
array([ -5., -5., -5., ..., -10., -10., -10.])
- time(time)datetime64[ns]2015-12-01 ... 2016-01-31T23:00:00
array(['2015-12-01T00:00:00.000000000', '2015-12-01T01:00:00.000000000', '2015-12-01T02:00:00.000000000', ..., '2016-01-31T21:00:00.000000000', '2016-01-31T22:00:00.000000000', '2016-01-31T23:00:00.000000000'], dtype='datetime64[ns]') - oni(time)float322.53 2.53 2.53 ... 2.53 2.53 2.53
array([2.53, 2.53, 2.53, ..., 2.53, 2.53, 2.53], dtype=float32)
- warm_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- cool_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- enso_transition(time)<U12'El-Nino warm' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['El-Nino warm', 'El-Nino warm', 'El-Nino warm', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
Checking background visc, diff#
staticfile = "/glade/campaign/cgd/oce/projects/pump/cesm//gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb/run/*static*.nc"
static = xr.open_mfdataset(staticfile).isel(time=1).squeeze()
static.Kd_bkgnd.cf.sel(Z=200, method="nearest").hvplot.quadmesh(
cmap="spectral_r", cnorm="log", clim=(5e-7, 2e-4)
)